Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare. Over the centuries, quests for answers have led us to take giant leaps. It was only in the last century that the discovery of antibiotics freed us from many of the dreaded diseases of the past. Still, in the context of recent published literature (e.g. accessible through PubMed), over 138 000 studies discuss medication errors, and over 450 000 include delayed treatment . Still, the problem of people dying from medical care gone wrong has been vastly underappreciated and not well recognized. Today, we stand on the threshold of the new medical revolution, just as big and far-reaching. Despite all of our scientific knowledge, much of medicine is still based on the treatment of symptoms and performing learned trials based on treatments, which works for most patients to bring symptom relief, reduce the risk of complications and improve survival chances, but not for all. To get new insights into disease taxonomy, etiology and pathogenesis, it is important to understand how diseases are related to each other. Breakthroughs in prescription medication, surgical treatment and mental health interventions are among the reasons we live longer. However, providing the correct in-time treatment plan for patients with knowledge about their current medications and drug allergies is currently a tedious and error-prone task [1]. The widespread growth of prescribing and consuming medications has increased the need for applications that support medication reconciliation. Furthermore, living healthcare issues include misdiagnosis, overtreatment, decreased productivity, under-utilized clinical data handling, significant cost and spending . These mMedical error is the third leading cause of death after heart failure and cancer . AccordiOver the years, biotechnology has evolved immensely. Computers are becoming faster in speed and micro in size, heterogeneity is increasing in datasets and their volume is growing robustly. These expansions are fueling the engine of artificial intelligence (AI) for discovering many technical refinements to solve complex problems in almost every field of life, including science and medicine. AI is the branch of computer science with the capability of a machine to imitate and even enhance intelligent human behavior. One of the expected roles in life and medical sciences is to deal with extensive research studies aimed at supporting real-time decision-making and producing solutions to complex problems through knowledge and data intensive computational and simulated analysis . HealthcTo effectively implement healthcare data analytic processes, various big data management challenges ,17,18,19Intelligent big data platforms are necessary to improve the quality and transition of healthcare by expediting investigation of active hidden factors in clinical data with machine learning algorithms to obtain actionable gap-based information about patients for early detection and prevention of constitutional disorders like cancer, and streamlining data sharing by developing efficient communication across healthcare units and scientific laboratories. Its application in healthcare could be another great leap in medicine and a transformational force for guiding personalized and population medicine with several computational benefits. In the recent past, multiple AI and ML-based efforts have been made for deciphering diseases to facilitate predictive diagnosis and thereby guide treatment factors, e.g. drawing disease relationships using clinical manifestations, EHR and data generated using wearable technology. To get a detailed overview of available academic solutions, we reviewed contributions and compared various AI and ML claimed solutions, approaches and discussions , and reaHealth intelligence can play a vital role at various levels of clinical research and analytics that can lead to significant improvements in achieving goals for the provision of better personalized and population healthcare. In the past decade, various operational and research-based healthcare data management and analytic systems have been developed in both academia and commercial sectors. Our interest includes comprehensive solutions that implement healthcare data analytics process; provide features to manage, analyze, visualize and share EHR in de-identified form; help in automatically capturing information about patient demographics, scheduled appointments, pre-exam questionnaire results, consulted providers, conducted lab tests, diagnoses, treatment plans, objective test results, medications, surgical procedures and claims; support the clinical decision-making process with AI techniques to create classifiers, which can be trained on structured clinical data generated from different clinical activities and can learn similar groups of subjects, associations between subject features and outcomes of interest; and apply natural language processing (NLP) methods to extract information from unstructured clinical data e.g. narrative text, such as physical examination, clinical laboratory reports, operative notes and discharge summaries.The authors emphasized the idea of embracing changes with the advancement in technology with the potential integration of AI into the field of healthcare in a way that is beneficial to each healthcare worker. They focused in utilizing AI to obviate repetitive tasks to enhance patient–physician relationship and increase practice of empathy and emotional intelligence. Authors focused on deep learning algorithm implementation with increased data flow that allows machines to self-develop a complex function with improved predictability, as long as a large amount of data is fed as input. They developed a deep convoluted neural network for skin cancer detection, image analysis for diabetic retinopathy evaluation, smartphone-based AI platform to measure adherence in patients on direct oral anticoagulants  and patiThis approach is focused on examining the essential structural changes in the healthcare systems that are necessary to unleash the full potential of ML in medicine. It emphasizes developing the concepts of ML in medicine, which may be centralized around the idea of personalized diagnosis and treatment on the basis of all known information about the patient and collective experience. Giving rationale, authors highlighted proof-of-concept models that have been tested so far, e.g. difficulties in finding a relationship between current ML models and traditional statistical models, need for a tremendous amount of data to train ML classifiers for establishing general and complex associations and training clinicians in AI for accurate data interpretation. Furthermore, they effectively debated about consumption of physicians’ valuable time due to increased features (e.g. check boxes) in EHRs for administrative and billing purposes, which might prevent them from providing the best quality care for their patients. At the same time, they claimed that the integration of ML in EHRs may lead to potential fear of overreliance as well as decreased vigilance of errors and automation bias. Authors claimed a training ML classifier at EHRs for pattern detection to allow physicians to anticipate future events in high-risk patients, to obtain accurate and comprehensive diagnosis and provide with a quick search engine in locating the pertinent information within a patient’s chart, less clicking, voice dictation and better predictive typing.Overview and implementation of AI and ML in healthcare for the extraction of big data, and aiding clinicians in providing better care delivery were recently highlighted by authors. They discussed use of support vector machine (SVM)- and deep learning-based model development for physiological data segmentation and analysis, disease progression prediction and diagnosis in radiology. Authors discussed processes, which include designing effective models to aid in the diagnosis based on the information that resemble certain diseases, image analysis and interpretation to improve the decision-making performance of clinicians. Authors also raised ethical concerns in utilizing ML, primarily in governance and management of big data and future of employment.A study analyzed different AI applications in healthcare and discussed their potential future outcomes. Authors argued that currently AI research in healthcare is mainly focused on only a few health conditions, e.g. cancer, neural disease and cardiovascular disease. They criticized that existing healthcare systems do not provide incentives for data sharing and have no structure for the implementation of AI. Further classified applications of AI consist of ML algorithms to extract and cluster useful information from a large patient population to assist in making real-time inferences for health risk alerts and health outcome predictions, perform principal component analysis and reduce diagnostic and therapeutic errors; SVM to determine model parameters and identify imaging biomarkers; NLP for text processing and classification; and deep learning for diagnostic imaging and electronic diagnosis.Managing large volumes of data is a huge challenge for doing big data analytics. A group of researchers used an ML algorithm for structured and unstructured big data analysis to identify a wide range of medical conditions and diagnosis from the large-scale EHR database, including information about test results, historical information, management plans, and billing codes, etc. Authors initially applied their algorithm for the identification of potential predictors and keyword associations in the EHR to find presence of pseudoexfoliation syndrome (PXF) by using NLP, least absolute shrinkage and selection operator. The reported outcomes were based on positive and negative predictive values of the algorithm, which were also validated by the glaucoma specialists. Authors highlighted the potential of their algorithm with its application for data mining and predictive analytics in many other applications as well.The application of cognitive computer systems has been very successful for approaching cancer diagnosis and treatment . According to a team of researchers, cognitive computer systems can support physicians by providing rapid access to accurate information and treatment procedures, assisting in decision-making. Such systems have the potential to distribute cancer knowledge into clinical practice and remote areas worldwide, by optimizing clinical research and trials with reduced bureaucracy and cost. They presented the possibilities of AI in future cancer care and research, which includes developing international cancer networks, identifying beneficial therapies for rare and highly aggressive cancers, observing different therapeutic outcomes by different parameters, analyzing associations of cancer with other disease-specific attributes, discovering new cancer etiologies, incorporating pertinent patient and cancer characteristics into clinic-based uses, conducting economic broad-based cancer trials, uncovering genomic and molecular events sensitive to existing or new treatments and analyzing and developing new treatment pathways. Furthermore, authors predicted that large-scaled AI databases may benefit cancer programs and help cancer treatment and research.EHR has a great role in improving the quality and cost of healthcare, advancing biomedical science and facilitating clinical research. Supporting this claim, this overview presented some examples of running and accomplished research projects, e.g. Electronic Medical Records and Genomics (eMERGE) Network, Strategic Health IT Advanced Research Projects (SHARP) Program and Health Maintenance Organization Research Network’s Virtual Data Warehouse Project. The authors also discussed multiple caveats in existing healthcare systems e.g. inaccurate data entry in EHRs; incomplete patient information in EHRs; incorrect diagnosis and medication codes ), and their conversion into research descriptions and vice versa; data extraction and integration from clinical notes in to EHR; multiple data sources in EHRs affecting data provenance; mismatches of data granularity with comparative research; and differences in research protocols and clinical care.Deep learning is a dominant ML approach, which has been greatly augmented in healthcare, analytic systems development and modeling tools. This review presented potential healthcare applications based on multiple driving factors, including learning digital imaging  with deep learning to facilitate effective decision-making and therapy, digitization of EHR and applying ML for curating and analyzing data sets, integrative heterogeneous data analysis using deep learning, applying deep learning for hypothesis generation by identifying novel associations to establish causation and causal pathways, appropriate deployment of AI and ML-based platforms for remote monitoring and digital consultations and improving performance of deep learning with exposure to larger datasets. Authors predicted safe, effective, efficient and humanistic care in the future with the successful application of ML.Deep learning has the potential to transform healthcare by outperforming clinical systems and modeling complex relationships among active hidden factors of data. This study highlights the background, workflow and challenges of deep learning with its successful implementation of digital image analysis, and some examples like analyzing fundus images of the retina to predict cardiovascular disease and identifying images for melanoma, basal and squamous cell carcinoma by matching sensitivity criteria. They suggested developing and training classifiers based on the deep neural network to make predictions on big clinical datasets by using the nonlinear features for modeling regularities. Authors claimed that in the coming years, with the continuous increase in the volume of datasets and without any enhancements to the basic learning techniques, more promising results can be achieved by ML.The data efficiency of deep learning can be used to augment information by improving diagnostic accuracy and efficiency by analyzing whole-slide pathology images, which cannot be easily observed by the human eye. Performance of deep learning at test sets of 129 whole-slide images was examined and compared for identifying metastases in hematoxylin and eosin–stained tissue sections of lymph nodes from women with breast cancer. Authors of the study optimized deep learning algorithms for obtaining impressive results, even better than the participating panel of 11 pathologists. They predicted a bright future of deep learning applications in digital pathology and discussed the need of new intelligent tool development for handling diagnostic sensitivity and specificity.ML real-time applications have been used to address different clinical questions by analyzing and finding nonlinear relationships in the EHR. A research study highlighted one important aspect that could be one of the ML drawbacks and that is the utilization of most of the ML algorithms to solve clinical problems when they were originally proposed for other matrices. The authors of the study provided some examples to justify their claim and discussed that the primary purpose of operational EHR systems development is to not support any kind of ML implementation for predictions. Due to the lack of robust and structured clinical data, authors showed uncertainty in achieving high-quality results with the use of ML. They stressed the need for new tools based on the ML algorithms especially designed with prior thresholds for improvements. They also stated that the present discovery phase needs to implement ML for better understanding of heterogeneous treatment effects to implement precision medicine.A quantitative review of the health informatics field uncovers the scientific growth in this field. As discussed, despite a long period of progress, there is still no proper characterization of the knowledge and no common language, which necessitates illumination of the knowledge structures, text mining methods, scientometric analysis, tracking tools, Internet of Things-based decision support systems and social network visualization to identify hidden patterns. The study conducted produced six clusters and nine research themes to support health information technology by improving patient safety by reducing medication errors, and associating decision support, knowledge representation, telehealth innovations and professional behavioral changes in medicine. For the provision of better-personalized treatment, authors suggested that the health informatics field should involve DL, new ML algorithms and advanced big data analytics.Various healthcare systems are generating heterogeneous data of significant volumes which demands exploitation of healthcare data for resource optimization, patient satisfaction, improved care quality and health outcomes. Recommended use of AI and ML for advanced health data analytics with the arrangement of active partners in the healthcare processes requires ubiquitous services, therapeutic decision support, ethnographic health surveillance, integration of health-related data sources, personalized and predictive medicine by learning non-linear associations and drawing causal relationships among inherent data elements. A team of scientists designed a health data analytics process involving a methodical order of data processing, modeling, and analysis steps categorized as data- and knowledge-driven methods for Decision, Predictive, Descriptive, Optimization, Comparative, Prescriptive and Semantic analysis. They advocated implementation of AI and ML-based analyses with the inclusion of health data pre-processing, selecting algorithm based on expected outcome, developing analytical models and interpreting results.k-fold cross-validation rate and lung cancer by analyzing clinical and gene expression data; SVM has been used for analyzing single-nucleotide polymorphisms (SNPs) with leave-one-out cross-validation for multiple myeloma, with k-fold cross-validation for breast cancer and with k-fold cross-validation for breast cancer by analyzing clinical and population data; the Bayesian network can efficiently analyze clinical and pathologic data with cross-validation for colon carcinomatosis and with k-fold cross-validation for oral cancer by analyzing clinical and imaging tissue genomic; blood genomic data; SVM hold-out for breast cancer by analyzing clinical, pathologic and epidemiologic data and for cervical cancer by analyzing clinical and pathologic data; graph-based SSL algorithm with k-fold cross-validation for colon cancer; breast cancer by analyzing protein–protein interactions (PPIs) and gene expression data and k-fold cross-validation for breast cancer by analyzing Surveillance, Epidemiology, and End Results (SEER); SVM with cross-validation for oral cancer by analyzing clinical and genomic data; SVM with leave-one-out cross-validation for breast cancer by analyzing genomic data; Bayesian network with hold-out for breast cancer by analyzing clinical, microarray data; decision tree with cross-validation for breast cancer by analyzing SEER; SSL co-training algorithm with k-fold cross-validation for Breast cancer by analyzing SEER. Authors acknowledged the active contributions of ML in accurate cancer susceptibility, recurrence and survival predictions. However, some concerns exist due to lack of external validation regarding the predictive performance of models utilizing integrated clinical and genomic data. They underlined that application of ML methods in cancer requires significant validation in order to utilize them in everyday clinical practice. Authors suggested data pre-processing with focus on data modification via dimensionality reduction and feature detection.ML can be applied to detect key features by predictive modeling of complex and heterogeneous datasets for progression and treatment of cancerous conditions, risks and outcomes. Many research groups have highlighted the implementation of different ML algorithms in cancer research for estimating unknown dependencies to predict new outputs of the system, including artificial neural network, Bayesian network, SVMs, graph-based semi-supervised learning (SSL) and decision tree. There are numerous ML real-world applications. The artificial neural network has been established for breast cancer by analyzing mammographic and demographic data with The underlying assumption here is that precision medicine will provide tailored healthcare to patients and will yield lower rates of associated adverse outcomes. A classic example of precision medicine is the customization of disease treatment for a single individual which in the old paradigm was a one-size-fits-all medicine; an effective treatment is the treatment known to benefit most of the target population. However, a certain treatment may actually yield benefit to only a few individuals. The rest of the population will not benefit from the treatment and may even incur adverse effects. This exemplifies the need for AI and ML-based systems bridging multiple domains in a secure environment for heterogeneous healthcare data analysis and visualization.Different healthcare institutions do not necessarily utilize the same EMR system with the possibilities of effectively communicating with each other, which makes it difficult for physicians to track patients’ overall medical history. A team of authors brought about the importance of population perspective and need of foundation platforms for precision medicine, which can support large-scaled clinical data integration, and communication between different EMRs accessible to patients through different health centers. Authors proposed a universal EMR platform development by integrating population perspectives for the establishment of protocols to identify subgroups that fit distinct clinical phenotypes of complex disease and provides avenue for different treatment methods. Authors suggested an additional fifth ‘P’—Population-wise Perspective—to the existing 4P precision medicine concept. They justified this claim by examining a study about identification of a type 2 diabetes subgroup through a topological analysis of a population’s EMR and determined three major distinct clusters . AuthorsPrecision medicine has been groundbreaking in tailoring individualized and effective medical treatments based on the characteristics of each patient, and different susceptibilities to a particular disease, e.g. trastuzumab for HER2-positive breast cancer. Many authors are convinced by the importance of information technology and ML for the implementation of precision medicine, which includes data storage and analysis for determining the association between disease outcome , identification of patient characteristics and optimal treatment. Furthermore, authors highlight the requirement for multidisciplinary collaborations between clinicians and researchers.The scientific community criticizes the current healthcare structure, to be based on one-size fits all, and not transitioning from trial and error to evidence-based medicine. The authors recognize the importance of innovative technologies  and essential roles of AI in precision medicine implementation. Authors proposed three-factor-based approaches, which include point of care, large-scaled clinical datasets for training classifier and analytics and finally building foundations for precision medicine. The author’s combined deep learning prediction with human pathologist’s diagnosis resulted in a success rate of 99.5% and reduced human error by 85% at an early stage. Authors also presented guidelines for implementing AI in precision medicine, which included creating ethical standards for AI applications, gradual and incremental development of AI and training medical professionals about AI.in silico datasets could allow the replacement of animal and human models when conducting clinical trials by generating virtual patients with specific characteristics that enhance the outcome of each study. According to them, 25% of drug discovery occurs by chance, which can be highly accidental. They signify using predictive, proactive intervention in healthcare through AI and clinical decision support system development for leveraging big data analysis to make better predictions on the potential outcome of patients, ultimately supporting better decision-making by the physicians.There has been a lot of debate over the current state of clinical decision support and how it can be improved in providing precision medicine. Many scholars have discussed involving ML in healthcare settings for the diagnosis and better treatment of chronic disease in clinical, translational and public health. Furthermore, they have highlighted the significance of big data management, privacy, de-identification and data sharing. Focus on early diagnosis of chronic conditions through proper extraction of clinical insights and utilization of Authors have reviewed and reported analysis of the different clinical decision support states, improvements in patient outcome and various limitations and challenges in Laboratory, Medication, Diagnosis, Radiology, Pathology, Clinical Evidence & Outcomes, and Procedures, based on the availability of different data structures and standards in healthcare systems. Challenges in the laboratory mainly involve incorporation of genetic results into EMR in a searchable way; tests conducted at external labs cannot be incorporated due to lack of standardization, not encoding lab tests with Logical Observation Identifiers Names and Codes (LOINC), missing genetic information in EHR. Challenges in Medication include over time and ineffective drug combination. Challenges in Diagnosis surround handling of International Classification of Diseases (ICD) codes, as not all codes are not billable and some diagnoses are not even encoded. Challenges in Radiology are of metadata mostly not formatted according to Digital Imaging and Communications in Medicine (DICOM), and reports are unstructured. Challenges in Pathology hold generation of unstructured reports, and frequent utilization of unstandardized nomenclature. Challenges in Clinical Evidence & Outcomes consist of insufficient data types, and unavailability of universally adopted data models. Challenges in Procedures mainly cover the process for approving new procedural codes. Authors have discussed the evolution of EHR to effectively delivering personalized treatment. With discussion of two case studies, they emphasized the implementation of precision medicine for the personalized delivery of care based on patient-specific patterns of disease progression and determination of precise therapies.Personalized medicine is a broad and rapidly advancing field in healthcare, primarily based on disease-related clinical, genomic, metabolomics and environmental information. However, failure to correctly identify disease is one of the major reasons for misleading diagnosis, treatment and prognosis for the patient. A research group presented essential components  for accurate disease examination needed for successfully integrating personalized medicine into everyday clinical practice. Utilizing ML approaches for pattern recognition and development of statistical models (sample size and effect size), creating a knowledgebase of all existing phenotype categories and disease, organization of clinical datasets of population size and open software platform development for statistical analysis of high-dimensional healthcare and multi-omics data are crucial for practical realization of precision medicine.One of the key technological advancements in the diagnosis of brain tumors was microscope-based analysis. Overcoming the visual limitation of such techniques for leading different classifications of a given sample by different individuals, a group of authors discussed ML as a precise solution for accurate diagnosis by analyzing molecular data. A previous study trained an ML classifier at a maximum number of images of tumors that were classified by physicians, as it is not possible to get precise conclusions, especially when tumor is histologically indistinguishable. Authors discussed the application of supervised ML for analyzing central nervous system (CNS) tumor-type genome-wide methylation data to identify methylation patterns. They mentioned application of unsupervised ML to search patterns in the data sets to develop classification categories. Authors suggested application of ML for molecular analysis and visual inspection, as the disease can be the manifestation of both molecular and cellular changes.Authors studied major concerns regarding data security like breaches in patient privacy and also presented the latest developments in the field of computational data protection. They discussed advancements in the era of big data for computer-aided diagnosis, and a recent concept of precision medicine for providing care for clinical, environmental and genetic characteristics. Authors emphasized developing new ML algorithms for computational analysis of genomic data to systematize the process of finding genetic similarities among patients with similar prognosis, or response to a treatment. They also stressed the need of implementing intelligent procedures to ensure the privacy of data, as they believe that data anonymization is not enough to guarantee unidentifiability due to auxiliary information. They discussed some existing solutions, which includes de-identification by data suppression, k-anonymization, learning from noisy data, homomorphic encryption, multi-party computation, cryptographic hardware and protecting genomic databases. Authors acknowledged the current contributions of ML but with the expectations for substantial improvements to address clinical data security problems and development of new significant ML models to address the challenges of gene variations and similarities among patients. They suggested combining the latest computational data protection principles with legal and ethical perspectives to construct a secure framework for data sharing.Resource management is very important in any field of life, especially in healthcare. Aligning people and technology with organizational goals can positively impact with efficient implementation of planned workflow in achieving on time high-quality results. However, inefficient resource management may lead to over-exaggeration of organizational resources, which includes, time, cost, manpower and computational, bench and infrastructure resources. Furthermore, it is important to address the ethical and data privacy challenges, when implementing traditional state-of-the-art and intelligent healthcare data analytics.Importance and utilization of AI to improve health outcomes in low-income settings and regions has always been a point of discussion. One research focus has been on clearly identifying problems and issues related to the integration of global healthcare, application and deployment of AI in real-time environments, high-quality healthcare data collection in under-developed areas, automatic data extraction from hand-written notes in local languages, availability of high-performance computing and cloud-based environments for data management and analysis, and construction of knowledge bases and expert systems . FurtherData science has demonstrated success in laboratory medicine and reinforcing its value in transforming the healthcare system. This is a review on the perspective applications of data science current problems in healthcare, which includes the need of significant computational power to contribute to solving data optimization problems, overfitting in experimental designs, lack of data standardization, large-scaled datasets for training ML classifiers and other concerns in clinical laboratories, e.g. protected health information challenges, financial limitations and ethical concerns. Authors discussed the concept that data science and AI mimics human processes and improves the process of decision-making. They reasoned the use of predictive modeling for better collaboration between hospitals without sharing data and complying privacy regulation through ML (supervised and unsupervised learning) for healthcare data  mining, analysis and optimization, identification of large patient clusters with certain disease characteristics, reducing medical errors and cost and improving efficiency of staff and resources. Data science has its own applicability and ethical challenges especially related to AI integration. AI encompassing data science when adopted can even improve human thought processes in efficient decision-making with different ML algorithms, data mining, knowledge discovery, improving complex analytical tasks and calculating clinical pathways. Authors of a study acknowledged success stories of ML in healthcare , weather forecasting, structural recognition, NLP, games, analyses of financial transactions and improvements in industrial processes by finding patterns, discovering inefficiencies, predicting outcomes and taking factual decisions. They provided examples of ML application in healthcare for the detection of cervical cancer , prediction of histological diagnoses  and radical hysterectomies with gene expression analysis , DNA (epigenetic modifications) and RNA  data analysis. ML is contributing to better personalized treatment by monitoring patient activities ; analyzing molecular biomarkers; predicting drug efficacies, treatment responses and disease pathways; and identifying molecular factors and genetic variants. The authors presented an ML framework, which includes defining tasks, matrices, models and datasets. They showed concerns regarding implementation of human rights by the Universal Declaration of Human Rights at the 1948 United Nations General Assembly for data privacy, protection, de-identification and encryption for data handling, collection and sharing. Authors emphasized the consideration of key ethical concerns, which include consenting patient, AI human warranty and regulation of healthcare data according to principles of bioethical law.The healthcare workforce crisis is widening across the globe. With the increase in the number of chronic and complex diseases, the demand towards efficient healthcare system is consistently growing. However, the lack of access to care, differing quality and doctor shortages are increasing worldwide. Some authors have presented AI as the solution in healthcare and discussed its progress in the direction of predictive and proactive interventions in clinical decision support systems. They reviewed AI approaches, dividing them into three categories , artificThere are multiple challenges of implementing AI in medicine. A research group underlined the obstacles and caveats, especially when applied in radiology, pathology, dermatology, ophthalmology, cardiology, gastroenterology and mental health. They predicted adaptation of AI by almost every type of clinician, which mainly includes deep neural networks for pattern recognition and analysis of medical images ; applying deep learning to EHR for estimating the risk of a patient’s hospital readmission; supporting doctors in decision-making for resuscitation; determining patients at risk of developing sepsis and other diseases; and predicting biological age, and critical diseases leading to death. Authors highlighted current challenges to the field of life sciences that can be solved by ML algorithms, like identification and isolation of rare cells, multi-omics data analysis, classification of somatic and germline mutations and gene–gene interactions and prediction of protein structure and PPIs, the microbiome and single cells. They also discussed the use of NLP in drug discovery by analyzing biomedical literature; mining molecular structures; predicting off-target effects, toxicity and right dose for experimental drugs; developing cellular assays; and using AI cryptography for determining unidentified drug interactions. Together with these advantages, authors also discussed formidable obstacles and drawbacks due to the field of AI, which includes data privacy and security, data hacking and breaches, uncertainty in use of black boxes of algorithms to resolve output, state of AI hype in validation and readiness for implementing in patient care. Authors anticipated the bright future of AI applications by predicting useful clinical outcomes in health systems, algorithmic interpretation of images and data, reducing errors, inefficiencies and cost.ML is considered a branch of AI encompassing algorithmic methods to solve scientific, healthcare and various other problems without traditional computer programming; this involves interplay between large population-specific datasets and model building. Suggested AI tool development should be based on incremental learning (ability to continuously improve with the inclusion of new data) to refine predictive accuracies. One study proposed analytics exercise starting with the identification of clinical problems, annotation of extracted healthcare data, application of appropriate ML algorithms and its effect on decision-making, addressing legal and ethical implications, assessment of ML effect in trial, designing freeze and submission of dossier for medical devices, training clinicians on use of ML tools and monitoring for adverse outcomes. Authors of the study presented ML algorithms suitable for different clinical applications and reported real-time clinical developments in oncology with the implementation of different AI and ML applications and algorithms. Authors discussed the importance of human–computer interaction in AI and ML platform development to effectively support decision-making processes in healthcare. They presented different real-time examples, which include HealthSuite, GE Healthcare, Lumada, DISCOVERY and CURATE.AI. They recommended model linking EHR in future ML platform development for addressing healthcare data analytics concerns.k-fold cross-validation for splitting one’s sample into two models; and deep learning models for feature extraction, development of graphical processing units, predicting in-hospital mortality, unplanned readmissions, prolonged stays, and discharge diagnoses. However, they showed lack of confidence in ML for shielding against the normal challenges in observational data analysis, as screening some concerns require evidence.Another study examined the implications of progress of AI in observational research design and healthcare databases, and implementation of ML for data classification and prediction in Real-World Evidence (RWE) to support clinical and regulatory decision-making. They discussed American Recovery and Reinvestment Act (ARRA) explicitly prohibiting cost-effectiveness and focusing on broad interventions for better diagnoses, treatments, disease management programs and healthcare organization models by mainly analyzing EHR and establishing patient-centered Outcomes Research. Authors discussed a wide range of increased size and nature of biomedical operational and research data, which requires efficient utilization of ML approaches to restructure, dimensionality reduction, clustering, modeling, linking, classification, analysis and predictions. Meeting RWE objectives, authors acknowledged ML as one of the powerful tools today for bringing significant improvement to care service. They exemplified ML with the implementation of SVMs to predict hospitalization; regression-based methods for reducing the risk of overfitting; The effective use of big data by digitizing, combining large hospital networks and implementing efficient analytical approaches has been widely successful. According to a team of scientists, there are tremendous benefits of such approach in detecting diseases at earlier stages; predicting risk for medical complications; managing individual and population health, avoiding frauds; and addressing numerous healthcare questions. Furthermore, it can reduce waste and inefficiency by determining clinically relevant and cost-effective ways to diagnose and treat patients, applying ML algorithms to predict models and analyzing EHR and disease patterns to discover adverse treatment effects. Authors also discussed the current challenges, covering unavailability of user-friendly and transparent real-time big data platforms; lags between data acquisition, collection, cleansing, processing and standardization; missing ability to manipulate data at different levels for granularity, privacy and security enablement and quality assurance; difficulties in the management of large, diverse and complex data with traditional approaches; changing healthcare reimbursement models; lack of professional tools, infrastructure and techniques to leverage big data effectively; and absence of dynamic analytics algorithms for efficient data modeling. Authors presented a conceptual architecture of big data analytics, from developing multi-source data input, transformation, structure, management and analysis using traditional SQL, OLAP, to data mining.Authors reviewed some of the factors driving wide adoption of deep learning and other forms of ML in the health ecosystem. They stated that ML has the potential of promoting changes in specialty that requires accurate prognosis models (e.g. oncology) and pattern recognition . However, authors anticipated some unintended consequences due to the application of ML-based decision support systems in healthcare, which includes overreliance on automation, more potential for decision errors by physicians, misleading diagnosis, misinterpretation of data, intrinsic uncertainty in medicine, inevitable intrinsic uncertainties and rationale and inscrutable outcome of ML algorithms. Authors presented unintended consequences in clinical and operational research support in reducing the odds and better implementing ML in medicine. Authors appealed for developing more precise analytics platforms for pathology images, next-generation radiology tools, converting EHR into reliable risk predictors and monitoring patients’ health through wearables and personal devices.The problem of integration and analysis of large-scale biomedical data located in heterogeneous data sources can be solved by intelligently conforming biomedical data to support physicians and researchers in conducting new studies and drawing new hypothesis, leading to novel interventions. This study lists challenges in biomedical data linking, which includes identification of potential sources, determination of linking values, lack of a national unique patient identifier and data privacy and security concerns. The authors also presented some potential solutions to these challenges and support personalized treatment, urging the identification of potential sources of health information, considering non-traditional data  to assemble a universal view of a patient and probabilistic linkage to the unique patient identifier problem. They recommended future (AI and ML) tools development to analyze biomedical data for better clinical decision-making.The current trend towards AI and ML algorithm development and application comes with its set of issues and challenges. Despite the helpful contributions of AI and ML in healthcare, they have some concerns, especially related to the ethical challenges, which include unexpected risks due to the utilization of algorithms in medicine originally proposed for some other development; likelihood of using ML as the source of the communal medical mind; mirroring human prejudices in decision-making by learning from different unforeseen biases; in extreme premature conditions relying on ML results can lead to fatal conclusions; mostly applied ML algorithms are designed to perform in unethical conditions; ethical strain of gaining industrial profits with clinical decision-support systems without informing its users; increase in ethically problematic outcomes due to constructed ML black boxes; and reimagining of confidentiality due to ML. A review justifies these limitations in AI and ML with some examples, e.g. Uber’s software tool Greyball and Volkswagen’s algorithm. Authors are hopeful that these issues will be addressed in future ML-based healthcare systems, with the involvement or professionals and researchers from policy enactment, programming, task-forces , etc.AI aimed to improve the intellectual capabilities and performances of machines to solve complex and big data-oriented problems by classifying interaction patterns among variables, learning from experiences, strategizing and predicting better orientations. AI has been in business for almost over 50 years, and its applications are heavily demanded in numerous fields of life, science, technology and medicine ,69. AI dIn the last few years, AI has become more popular and seriously considered for analyzing diverse clinical data  for accurate diagnosis and effective treatment in different practices , e.g. raML is a branch of AI that utilizes and proposes different algorithms for learning from numerous data variables and revealing multifaceted relationships among data features to predict accuracies in different contexts and support decision-making processes. The overall ML process starts with the manifestation of either or both data and labels, training classifier to learn and model using an algorithm and then performing data evaluation and analysis to estimate final results. ML is mainly categorized in three learning approaches: classification, cluster, and regression . ClassifML is becoming the transformational force in healthcare  for guidSVM  is one oDeep learning is a dominant approach based on artificial neural network. It was inspired by the ability of the brain to learn complicated patterns, and one of the most popular ML algorithms in healthcare today, aiming to advance clinical medicine and care delivery . The artLogistic regression is a statistical method to assess the relationships between various predictor categorical and continuous variables and dichotomous binary outcome . It has Discriminant analysis is a widely applied technique in medical studies for pattern recognition . DiscrimThe decision tree utilizes a tree structure modeling approach with conditional control statements for establishing an efficient decision-making process . The decRandom forest is also known as the random decision forest, a combination of algorithms to build predictive models for classification and regression problems . Its claLinear regression is an ML approach to model relationships between dependent and independent variables using linear predictor functions to identify errors of prediction in a scatter plot and characterize relationships among multiple factors . Linear Naïve Bayes is a supervised ML technique based on Bayes’ theorem for data mining, classification, and predictive modeling  to find KNN is also a supervised learning-based algorithm used for classification and regression in pattern recognition, data mining and intrusion detection by classifying points to given categories from a training dataset . Its ML HMM was originally proposed to solve speech problems by making complex and instinctive probabilistic models for finding and processing hidden states and paths ,203,204.The genetic algorithm was inspired by Charles Darwin’s theory of natural evolution to solve constrained and unconstrained data optimization and standardization problems by repeatedly modifying a population of individual solutions . PrimariAI and its application in healthcare could be another great leap in medicine and a transformational force for guiding personalized and population medicine with several computational benefits. The extent of its popularity in healthcare can be easily determined by the number of AI related published work in medicine. At the time of the study (09 April 2019), a total of 16 166 AI and ML papers were available through PubMed. The growth of scientific literature in AI increased in the last 10 years with 14 469 papers (2009–2019), and more than 70% work was published in the last five years. While the growing importance and relevance of AI in healthcare is indisputable, to improve public sector clinical practice, there is a critical need for development of intelligent frameworks to connect operational and analytical healthcare systems in a way that experts from multiple domains can perform measurement and predictive analysis. AI has the potential to play a vital role at various levels of clinical operations, research and analytics to achieve significant improvements in providing better individualized and population healthcare at lower costs, and together with better work-life style for clinicians and staff. The major barriers in successfully implementing AI in healthcare include gaps in AI building blocks and infrastructures; low quality data availability; business model sustainability; regulations and policies for data collection, usage and sharing; disruption in the physician–patient relationships; integration to operational health systems; reduced evidence and reproducibility; selection of most appropriate ML algorithm; lack of understanding of AI and ML processes to predict; hazard of dehumanization of healthcare data and job insecurity threat; conflicts of interest and impartial access; accountability exploitation of AI; ‘sanity’ check to minimize any bias; handling of misleading and erroneous results; and data privacy, ethics, consent and ownership ,239,240.Determining which AI approach to use for which task is a challenge in itself. Classifying tasks based on available predictor variables is a key step to correctly addressing the problem. Traditional AI models can be used for simple prediction tasks while complex tasks require more complex models. The next step is to consider how the model will be used in practice. Setting rules to identify if a task needs to pre-process data before execution or if a complex task can be broken down into simple tasks is amenable to a traditional model. Creating and updating these rules is very time-consuming but is very useful. Availability of training data is another key factor in intelligent automation. Simple models can work with little data and few variables, but complex models require huge amount of data with multiple examples and scenarios as it is expected to remove noisy data and learn to identify complex statistical patterns . NumerouAlthough many AI and ML-based approaches have embraced the data gold rush in biomedicine, many concerns remain, which include integration of biomedical data located in heterogeneous data sources; handling extensively available irrelevant, error-prone and missing data; unnecessary follow-up diagnosis and treatment as a result of overloaded health information; loss of data privacy and reimagining of confidentiality; ethical strain of gaining industrial profits with clinical decision-support systems without informing the users; possibilities of patient data exploitation, employment insecurity due to governance and management of big data; regulation of healthcare data analysis according to principles of bioethical law; evidence-based observational data analysis and screening, slow data processing speed and difficulties in determining optimal parameters; difficulties in understanding structures of algorithms; possibility of too many overfitting attributes and its optimal network structure; handling continuous explanatory variables with more than two levels; difficulties in understanding odds and probabilities ; correctPotential solutions to these concerns are based on design and implementation of healthcare IT infrastructure ,49; EMR We also considered most adaptable traditional  AI and ML algorithms  in healthcare and justified their contributions with living examples for different clinical applications, e.g. deep neural network for diagnosing eye diseases (diabetic retinopathy) by analyzing retinal images; diagnosing cardiac anomalies by analyzing MRI images of heart ventricles; detecting malignant lung nodules by analyzing radiographs; grading prostate cancer tumors by analyzing ultrasound of biopsy cores; and producing glioma survival predictions by analyzing histological imaging and genomic marker data. Some other non-traditional algorithms were used, e.g., ‘Watson for Oncology’ ,242 and The goal of this study is to highlight recent contributions and effectiveness of AI and ML in the development of computational systems towards better healthcare and precision medicine. We have reviewed and discussed multiple AI and ML-based approaches ,2 and 3 To effectively meet the goals of healthcare data analytics, while dealing with the aforementioned community, and traditional and AI-based challenges, significant efforts are required from experts in multidisciplinary sciences. To facilitate and improve public sector clinical research and practice, there is a critical need for academic frameworks that can connect operational and analytical systems in a way that experts from multiple domains can perform measurement and descriptive analysis, even without strong computational background. There is a need to develop standalone, user-friendly, standardized, open-source, and comprehensive solutions, which implement healthcare data analysis by connecting all kinds of patient data generated from any of the existing commercial EHR systems at any level, which includes patient’s demographic information, personal life style, medical history, recent visits to the practices, providers attended, diagnosis performed, lab tests conducted, longitudinal images, medications and procedures, samples taken for wet and dry lab experimentations for research and treatment of disease with no cure. Furthermore, the ideal system should be capable of automatically linking and communicating with similar systems.Unmet clinical research and operational data analytics needs development of intelligent and secure systems to support practice transformation for implementing precision medicine at a global level. Overarching goals include new multi-functional platforms founded on the clinical, AI and scientific premise that integrates and analyzes heterogeneous clinical data received from multiple platforms. We need to implement and train AI classifiers at available structured clinical dataset of over a million subjects to help in early detection and diagnosis of common, frequently occurring and rare diseases and predicting the performance of provided treatments. We need to further explore AI methodologies to design models segregating disorders, identify causative medical conditions and determine the best drug therapies, especially when adjusted for age, race and gender. The goal should be to accelerate clinical care and discovery by satisfying research aims, improving quality and transition of care, obtaining actionable care gap-based information about patients and developing communication and coordination across hospitals, specialists, community-based providers, sub-acute care, nurses, quality inspectors, management, researchers and analysts. We need to strive to address possible challenges that continue to slow the advancements of this breakthrough treatment approach.Along with AI- and ML-based methodological developments, it is important to address the issues related to healthcare data privacy and security. In most of the cases, academic and applied research environments do not have access to the healthcare data. The major reason is secure handling of protected health information (PHI) and lack of trust by the healthcare institutions in providing access to their medical records. We need to develop secure research-based HIPAA-compliant frameworks for efficient PHI storage, pre-processing, de-identification and integration to serve a large community of users, support organizational policies and provide efficient access and connectivity. It is mandatory to implement HIPAA rules for system user data security, which includes application and data criticality, risk management and analysis, information system activity review, contingency plan, device and media controls, disaster recovery plan, data backup plan, emergency mode operations plan, device and media controls, access controls, security incident procedures, vulnerability assessment, penetration testing, physical security, business associate agreements, polices and procedures, evaluation, audit, assignment of responsibility sanctions, workstation use and security. Furthermore, it is important to provide an external layer to the overall healthcare data analytics systems for placing clinical data in a distributed centralized structure; administering security by encrypting data as well as offering a multi-user-based graphical interface with controlled access; and supporting data backup with redundant data management plans.Precision medicine is progressing but with many challenges lying ahead , which rSupplementaryMaterial_Table_3_baaa010Click here for additional data file.


The pioneering Age-Friendly University (AFU) initiative, endorsed in 2016 by GSA’s Academy for Gerontology in Higher Education (AGHE), calls for institutions of higher education to respond to shifting demographics and the needs of our aging populations through more age-friendly programs, practices, and partnerships. Over 65 institutions have joined the AFU network and adopted the 10 AFU guiding principles. This symposium will feature leaders at AFU campuses representing centers on aging and gerontology programs who will discuss why their institution joined the initiative, their age-friendly campus vision, and their recommendations for mounting an AFU initiative. AFU Director Charness (Florida State University) will describe launching an AFU initiative from the perspective of a state university, with a focus on critical discussion points, the concerns of upper administration, and the timeline for completion of the process. AFU Director Porter (University of Manitoba) will discuss how the Centre on Aging has been using its mandate as a research centre to advance the age-friendly movement through research, knowledge mobilization, training, and partnership initiatives. AFU Director Schumacher  will talk about how the AFU framework has served as a platform for new and constructive synergies among gerontology programs, centers on aging, health systems, and higher education. AFU Director Gugliucci (University of New England) will discuss strategies for leading health professions education programs within an AFU framework, with special attention to AGHE resources available to support these efforts. Directors of Aging Centers Interest Group Sponsored Symposium.


Senegal has been an exemplar country in the West African region, reducing child stunting prevalence by 17.9% from 1992 to 2017.In this study, we aimed to conduct a systematic in-depth assessment of factors at the national, community, household, and individual levels to determine the key enablers of Senegal's success in reducing stunting in children <5 y old between 1992/93 and 2017.A mixed methods approach was implemented, comprising quantitative data analysis, a systematic literature review, creation of a timeline of nutrition-related programs, and qualitative interviews with national and regional stakeholders and mothers in communities. Demographic and Health Surveys and Multiple Indicator Cluster Surveys were used to explore stunting inequalities and factors related to the change in height-for-age z-score (HAZ) using difference-in-difference linear regression and the Oaxaca-Blinder decomposition method.Population-wide gains in average child HAZ and stunting prevalence have occurred from 1992/93 to 2017. Stunting prevalence reduction varied by geographical region and prevalence gaps were reduced slightly between wealth quintiles, maternal education groups, and urban compared with rural residence. Statistical determinants of change included improvements in maternal and newborn health (27.8%), economic improvement (19.5%), increases in parental education (14.9%), and better piped water access (8.1%). Key effective nutrition programs used a community-based approach, including the Community Nutrition Program and the Nutrition Enhancement Program. Stakeholders felt sustained political will and multisectoral collaboration along with improvements in poverty, women's education, hygiene practices, and accessibility to health services at the community level reduced the burden of stunting.Senegal's success in the stunting decline is largely attributed to the country's political stability, the government's prioritization of nutrition and execution of nutrition efforts using a multisectoral approach, improvements in the availability of health services and maternal education, access to piped water and sanitation facilities, and poverty reduction. Further efforts in the health, water and sanitation, and agriculture sectors will support continued success. Supplementary Appendix 1) following the devaluation of the CFA franc in 1994 and moderate poverty reduction with an increase in remittance income  from 1993 to 2011 using data from demographic and health surveys (DHS) . The autPanel 1Systematic Literature Review of Stunting DeterminantsOur literature review found that the basic causes of stunting in Senegal included climate shocks including rain, droughts and extreme cold , 42, ecoLiterature indicates that the underlying causes of stunting were lack of access to clean water, maternal and child healthcare, duration of breastfeeding, and age of introduction to complementary feeding. Two national studies did not find piped water, open defecation, or access to a toilet to be associated with stunting , 43. ConImmediate causes of stunting in Senegal include anemia , 54, int1) quantitatively examine determinants of stunting reduction in Senegal and decompose long-term stunting change into relative contributions from key drivers; 2) generate a systematic landscape of the major stunting-relevant policies and programs in Senegal, focusing on both nutrition-specific and nutrition-sensitive initiatives; and 3) explore national- and community-level perspectives on Senegal's nutrition evolution (focused on progress in stunting) and the major contributing factors behind it.Due to Senegal's progress across several sectors, a multifactorial stunting reduction success story is likely, warranting the exploration of these factors using a mixed-methods approach. However, to our knowledge, no previously published national studies have executed this approach to examine a wide array of stunting determinants by using both quantitative and qualitative methods or over a time period between 1992/93 and 2017. The vast majority of previous studies were based on subnational cross-sectional surveys and were not generalizable to the population of Senegal. Hence, in our study we aimed to do the following: In this mixed methods study we applied several complementary approaches to inform study objectives, including a systematic literature review, a retrospective quantitative analysis, a program and policy analysis from 1990 to 2017, and qualitative data collection and analyses. An adapted conceptual framework , 50. RefEthics approval for the study, inclusive of primary data collection, was obtained through the Ministry of Health Ethics Board of the Cheikh Anta Diop University. Ethics approval for the broader stunting case study was also obtained through the Research Ethics Board at the Hospital for Sick Children (SickKids), in Toronto, Canada.1) stunting , 2) child , and 3) Senegal , we searched >15 online databases and grey literature sources. After deduplication, 1728 records were found, of which 44 articles remained after full text review. Full methods and results of the systematic review are included in We conducted a systematic scoping literature review between November 2017 and December 2018 of published peer-reviewed and grey literature to synthesize information on contextual factors, national and subnational interventions, policies, strategies, programs, and initiatives that may have contributed to reductions in child stunting in Senegal from 1990–2017. Using the search categories of Senegal has had multiple sequential DHS performed over the past 3 decades, including serial DHS since 2012. We selected DHS that were considered good quality and that aligned with our study period  as the primary quantitative datasets used in this study. Available anthropometry data for children <5 y old by survey round are presented in We studied child HAZ and stunting prevalence (HAZ < −2SD) estimated using WHO child growth standards as the main study outcomes . PotentiWe estimated child HAZ kernel density plots and HAZ compared with age plots (“Victora curves”)  using smSupplementary Appendix 3 and the Methods paper in this series  for fulKey nutrition-specific and -sensitive policies and programs that took place between 1990 and 2017 were assembled into a timeline using an iterative approach. The Senegal study principal investigator and research team members proposed a timeline based on a systematic desk review of literature. This timeline was reviewed by expert stakeholders and iterated on until agreement between the Senegal research team and country experts was reached.Supplementary Appendix 4.Qualitative interviews were conducted to gain perspectives of key national stakeholders, regional stakeholders , and mothers in the community. Participants were identified and selected using purposive sampling strategies , includi). In 1992/93, the growth faltering period lasted between 6 and 20 mo, with HAZ decreasing at a rate of 0.10 SD per mo  and continued, although at a slower rate of 0.02 SD per mo  between 20 and 29 mo . Senegal made major strides in improving health sector development, organizing poverty reduction initiatives, implementing campaigns for improving population education, and safe water and sanitation projects. The government's early adoption of coordinating bodies for nutrition and multisectoral action plans for fighting malnutrition  have also been pivotal.A comprehensive list of nutrition-specific and -sensitive policies identified between 1990 and 2018 is shown in Panel 2Policy and Program ReviewFollowing the 1994 devaluation of the CFA franc and the cumulative effects of climate shocks, the nutrition situation in the country was troublesome . This leThe health sector's performance, sustainability and governance were targeted by the National Health Development Plan . Key povSupplementary Appendix 7.The qualitative analysis highlights diverse contextual and distal policy and program efforts and underlying and immediate causes of stunting decline from the perspectives of national and regional stakeholders and mothers, including improvements in political context and stability, women's empowerment and education, poverty reduction, household access to clean water and sanitation, and improved food security and dietary intake related to greater household income. See Panel 3Qualitative Inquiry ResultsNational Expert StakeholdersNational stakeholders credited political stability and lack of large-scale conflicts, moderate poverty reduction, and women's education and empowerment to facilitating improvements in chronic child malnutrition. Political will through the creation of the CNLM and the CLM leading to multisectoral action was a key driver of stunting reduction.“...The government set up since 200[1] a structure to promote a multi-sectoral approach in the fight against malnutrition. This unit brings together all the technical ministries that have a role to play in the fight against the determinants of malnutrition. This structure is highly set up at the level of the Prime Minister's Office, which facilitates the coordination of sectoral interventions. So the state of Senegal has [from] very early [on] understood the advantage of this multi-sectoral approach to the fight against malnutrition...” [CLM representative]The PNC, led by the CNLM, the PRN, iodized salt program and the food security program (NESA), led by the CLM, were central programs that reduced undernutrition. Several nutrition sensitive policies and programs facilitated cross- and multi-sectoral collaboration including the education, health, and hydraulics sectors. Support from bilateral and multilateral donors for nutrition programs/projects was integral in the success in reducing stunting. However, challenges in the implementation of programs included: inequitable coverage and access to programs, competition and dynamics between actors and institutions, limited monitoring and accountability of community actors, limited involvement of local authorities, limited sustainability, inadequate support and training for nutrition experts, challenges with health promotion and social behaviour change communication, and failure of donors to align with national priorities. The country has had moderate improvements in access to basic resources including drinking water and sanitation facilities, access to health services through the role of community health workers and their promotion of exclusive breastfeeding, and training and availability of nutrition experts. Consequently, decreased fertility, increased spacing between births and the reduction of communicable infections were expressed as important factors that led to stunting gains with further efforts required to address micronutrient deficiencies including iodine deficiency and anemia.Regional StakeholdersRegional stakeholders agreed with the national stakeholders on the positive effects of poverty reduction, maternal education and programs such as the CLM, the PRN and other donor-led initiatives on stunting reduction. They also indicated improvements in access to health services and hygiene practices such as handwashing."There are families who know and apply the rules of hygiene inside the house. There are others who apply little or no hygiene, but there have been changes in the meantime." However, cultural practises and poverty have limited progress towards diet diversity and exclusive breastfeeding despite recommendations provided by health workers.“Most moms do not ask for advice about breastfeeding. This is due to their ignorance. On the other hand, young mothers start asking questions about exclusive breastfeeding, because they have a higher level of education.” [ASBEF NGO Supervisor]Mothers in CommunitiesFocus groups with mothers revealed poverty reduction as essential to food security which has seen some improvements. Although the availability of food has improved, it is dependent on geographic location and seasonality. However, challenges with water, sanitation and hygiene were observed across the three regions and age groups.“We cannot have good health without good sanitation, also in terms of food it is difficult for us to find fruits and vegetables, garbage in the street is also a problem, it is difficult to find fruit. The salinization of water is [also] a problem in this region.” Mothers mentioned improvements in the availability of health posts as most had received 4+ ANC visits, had SBA deliveries, had their children vaccinated, and had nutrition-related advice provided to them.“The diet has improved because during immunization sessions, you are advised the foods that must be given to the child.” Using a mixed-methods approach, in this study we identified factors related to Senegal's stunting decline in transitionary periods such as 1992/93–2005, when stunting declined significantly, to more stagnant periods such as from 2005 to 2017. To begin, the political stability experienced by Senegal fostered an environment supportive of health and nutrition policies. Specifically, as early as 1994, the government demonstrated the political will to institutionalize nutrition and have multiple sectors involved with the creation of the CNLM. Improvements in community health service availability, maternal education through increased primary and secondary school enrollment, access to piped water and sanitation facilities, and poverty reduction have been observed over our study period and supported the reduction in stunting.This mixed-methods study, to our knowledge the first of its kind performed in Senegal, incorporated robust quantitative analyses; multilevel perspectives from qualitative interviews and focus groups; an in-depth program and policy review; and the first systematic review of existing literature. A number of our study strengths were attributed to the quantitative methods. First, combining the DHS and MICS surveys allowed for an analysis over periods of peaceful democratic transitions , political commitment toward achieving the Millennium Development Goals (MDG), and substantial prioritization of cross- and multisectoral nutrition-focused efforts. Second, our use of piecewise linear splines on the Victora growth curves allowed for identification of inflection points and comparison of the rate of HAZ change per month across age groups and time periods. Third, a comprehensive set of predictors of stunting were included, allowing us to account for possible pathways in the hierarchal regression models. However, the effects of food security, agriculture, intrauterine growth, maternal height and BMI, and nutrition and health programs could not be quantitatively examined due to data unavailability for our full time period. Proxies were used where possible, i.e., 24-hour recall was used to measure food security. A post hoc decomposition analysis revealed that maternal height contributed to nearly 6% of the explainable (57%) stunting decline from 1992/93 to 2005, suggesting that maternal nutrition may also have an important role. It should be noted that although a wide range of confounders were controlled for, there were possibilities for residual confounding and interactions between factors within and between hierarchal levels in our decomposition models. Last, the DHS 2010/11 could not be used in our analysis due to poor data quality, as there were >20% flagged implausible values for child anthropometry data. Accordingly, we used the most recent Senegal DHS (2017) as an endpoint for our decomposition analysis, which has not previously been used. To increase our confidence in the surveys we used, the quality of anthropometry data was rigorously examined for accuracy of height and age data .Senegal's overall political stability has ensured a stable and secure context, making the country attractive to bilateral donors for nutrition, health, and agriculture programs . Only on1) establishing a foundation (1950–1970s); 2) implementation of a curative approach (1970–1990s); 3) institutionalization of nutrition (1990–2000s); 4) intensification and decentralization (2000–2010s); and 5) adoption of a multisectoral approach (2010–present)  . Nutritipresent) . Despiten 1950–19s; 2) imppresent) .Access to and use of reproductive, maternal, newborn, and child health (RMNCH) services have remarkably improved from 1992 to 2017, with a 4-fold increase in the proportion of women attending 4+ ANC visits and a slightly smaller increase in SBA births . These iFemale literacy has modestly improved and may be attributed to several national efforts, including the National Action Plan for Education for All, the Letter for the Education and Training Sector Policy, and the Ten-Year Education and Training Plan . ChildreSenegal has had moderate improvements in sanitation facilities and access to piped water over the past few years, achieving the MDG target of 7.c by focusing on larger population centers. WASH initiatives in the country led to improved management and coverage of potable water, and in more recent years, the sewer system. In our study, improved access to piped water and reduction in open defecation contributed to the stunting decline between 1992/93 and 2017; and an earlier study of stunting determinants between 1993 and 2011 also found piped water to be a key driver of change . The relSenegal has experienced steady GDP growth following the devaluation of the CFA franc and a reduction in the poverty headcount ratio at national poverty lines ($1.90 per d) from 68% (1991) to 38% 2011) ( (11). ReInequalities in the prevalence of stunting have persisted across wealth quintiles, maternal levels of education, and area of residence. Children from the wealthiest families, those whose mothers have secondary or higher levels of education, and those living in urban areas are less likely to be stunted than their counterparts.While the health system in Senegal has been improving, challenges in equitable targeting of resources remain. The 1996 decentralization reform  led to the transfer of financial resources, responsibility, and autonomy for 9 policy areas, including education and health , 78. HowSenegal is highly reliant on agricultural production; however, early agriculture programs have struggled to meet their targets and have not effectively contributed to the long-term growth of the sector . Recent Senegal's success in reducing the burden of stunting is credited to the enduring prioritization of nutrition in national development plans, implementation of national nutrition-specific and -sensitive interventions, and the targeting of the most vulnerable populations. The execution of a multisectoral approach has been essential in facilitating the prioritization, coordination, and implementation of nutrition efforts across diverse sectors and corresponding stakeholders. Future studies may focus on subnational drivers of stunting reduction, examine other outcomes such as wasting occurring concurrently with stunting, and perform cost-effectiveness analysis of programs to determine where future investments would be useful.nqaa151_Supplemental_FileClick here for additional data file.


In December of 2019, there was an outbreak of a severe acute respiratory syndrome caused by the Coronavirus 2 (SARS-CoV-2 or COVID-19) in China. The virus rapidly spread into the whole World causing an unprecedented pandemic and forcing governments to impose a global quarantine, entering an extreme unknown situation. The organizational consequences of quarantine/isolation are: absence of organized training and competition, lack of communication among athletes and coaches, inability to move freely, lack of adequate sunlight exposure, inappropriate training conditions. Based on the current scientific, we strongly recommend encouraging the athlete to reset their mindset to understand quarantine as an opportunity for development, organizing appropriate guidance, educating and encourage athletes to apply appropriate preventive behavior and hygiene measures to promote immunity and ensuring good living isolation conditions. The athlete’s living space should be equipped with cardio and resistance training equipment (portable bicycle or rowing ergometer). Some forms of body mass resistance circuit-based training could promote aerobic adaptation. Sports skills training should be organized based on the athlete’s needs. Personalized conditioning training should be carried out with emphasis on neuromuscular performance. Athletes should also be educated about nutrition (Vitamin D and proteins) and hydration. Strategies should be developed to control body composition. Mental fatigue should be anticipated and mental controlled. Adequate methods of recovery should be provided. Daily monitoring should be established. This is an ideal situation in which to rethink personal life, understanding the situation, that can be promoted in these difficult times that affect practically the whole world.  In December of 2019, there was an outbreak of a severe acute respiratory syndrome caused by the Coronavirus 2 (SARS-CoV-2 or COVID-19) in Wuhan, Hubei Province, China. The virus rapidly spread across the country and then into the whole world . The heaThe organizational consequences of quarantine/isolation are: absence of organized training and competition, lack of adequate communication between athletes and coaches, inability to move freely, lack of adequate sunlight exposure, inappropriate training conditions ,5. StayiTherefore, first, it is extremely important to identify these effects and to understand the mechanisms and effects on all physiological systems, as well as their impact on athletic performance. Second, but no less important, it’s important to provide practical recommendations to coaches and athletes to reduce the unwanted consequences of the forced quarantine.The principle of training reversibility states that stop or markedly reduce training induces a partial or complete reversal of the previous developed adaptations, thus compromising athletic performance . The rev2max) and one repetition maximum (1-RM) bench press in healthy adults, independent of participant and load characteristics, as shown in the authors’ review and meta-analysis [Detraining affects different physiological systems  and their corresponding physical capacities . Although some investigations have concluded that neural changes are long-lasting and did not affect the elements of H-reflex pathways , there ianalysis . Therefoanalysis .2max have been described within 2 to 4 weeks of detraining [2max; (2) decrease in blood volume; (3) changes in cardiac hypertrophy; (4) decrease in the total hemoglobin content; (5) decreased skeletal muscle capillarization; and (6) disruption of temperature regulation. When absence of training continued beyond 2 to 4 weeks [2max; (2) reductions in maximal arterial-venous (mixed) oxygen difference; (3) changes in maximal oxygen delivery, which may result from decreases in total hemoglobin content and/or maximal muscle blood flow and vascular conductance; (4) declines in skeletal muscle oxidative enzyme activity; and (5) reductions in submaximal exercise performance, which may be related to changes in the mean transit time of blood flow through the active muscle and/or the thermoregulatory response  to exercise. Therefore, athletes must incorporate some type of endurance exercise their daily routine to try and reverse some of the aforementioned effects of detraining. Reductions in maximal and submaximal exercise performance occur within weeks after the cessation of training. These losses in aerobic performance decline cardiovascular function and muscle metabolic potential. Specifically, significant reductions in VOtraining . The det 4 weeks , the detFlexibility is the ability to move a joint through its optimal range of motion. The ability to move a joint without restriction or pain depends on the condition of different structures, such as bone, muscle, and connective tissue. It also depends on the muscle’s ability to produce an adequate amount of force . DecreasShort-term detraining may specifically affect eccentric strength and the size of the Type II (FT: fast twitch) muscle fibers . It has Reduced or complete absence of strength training can cause loss of muscle mass. Muscle atrophy results from an imbalance between protein degradation and synthesis in favor of the former . When inPeriods of prolonged inactivity negatively affect the anti-gravitational muscle groups and the posterior extensor muscle chain . In geneReducation in activity results in a reduced energy expenditure, which consequently requires a reduction in energy intake to prevent unwanted body fat gains. In terms of an absolute amount of protein per day, when increasing protein to 2.3 g/kg, body mass reduces muscle loss during periods of reduce caloric intake . Thus, aA major consideration when training athletes in home isolation is compliance, especially regarding the intensity and volume of exercise. It is difficult to monitor and ensure that the load that athletes use at home is appropriate to maintain physical fitness and performance at the required level. For recreational and ordinary people in isolation, maintaining an acceptable level of physical fitness is possible with moderate exercise , but higLack of competition poses an additional problem to teams and athletes, because it is through competitions that athletes can best maintain their physical fitness and sport form. Competing activities in many sports with a congested competition schedule  is also In spite of everything, some positive effects of isolation should also be kept in mind. In such conditions, the athlete can fully recover from all stresses, injuries and previously accumulated loads (overreaching and overtraining). For example, in team sports there are very few situations in the regular annual calendar in which a player can have a prolonged period of complete recovery from specific training and competition demands. Only off-season/transition periods can offer some opportunity for rest . Isolati-Encourage, provoke and motivate the athlete to reset their mindset and use this break as an opportunity for personal development ;-Organize appropriate guidance and support to athletes by experts  by using technology ;-Educate and encourage athletes to apply appropriate preventive behavior and hygiene measures to promote immunity and protect their own health and the health of the people in their immediate environment ;-Ensure good living conditions in isolation . If possible, the athlete’s living space should be equipped with cardio equipment , resistance training equipment  and other equipment for frequent use . If not, some forms of body mass resistance circuit-based training could promote (or maintain) neuromuscular and aerobic adaptations ;-Organize alternative sports skills training  based on the athlete’s deficits and needs;-Organize personalized strength and conditioning training at home with available space and material resources that are tailored to the athlete’s individual characteristics and current needs . Focus o-Educate the athlete about nutrition, supplementation  and hydration in isolation conditions, and about strategies to control body mass and body composition ,36,48,49-Organize mental fatigue monitoring and mental training  ;-Provide adequate methods of recovery  ;-Use forms of self-assessment  that an athlete can use on a daily basis and share data with a strength and conditioning coach ;-Establish daily monitoring of the athlete’s health, wellness, physical fitness, recovery  and work-Even though many athletes are not currently injured, the time off is similar to the time off after an injury ;-Finally, muscle memory is important to educate athletes, given that any losses are rapidly regained. This should quell some anxiety .Based on the current scientific and practical evidence, we strongly recommend the following points:To conclude, an athlete’s life in isolation due to a COVID-19 crisis and imposed quarantine should have another, positive meaning. This is an ideal situation to rethink and reorganize one’s personal life and value system. Humility, gratitude, understanding of the global situation, empathy for other people, family values, attitude towards knowledge, spirituality, and helping the needy are just some of the values that can be promoted and truly lived in difficult times that affect practically the whole world. As athletes are often role models, in this challenging time they should take responsibility and promote “good values”. Thus, they can contribute to building better societies and better people and promote better human behavior.


Vibrio cholerae, is transmitted by the faecal-oral route through contaminated water or food. Cholera annually affects 2.9 million people, causing 95,000 deaths worldwide. Forcibly displaced populations experience high rates of cholera due to limited access to healthcare and poor living conditions, including overcrowding, and disruption of water, sanitation, and hygiene (WASH) services. Cholera is preventable and predictable; however, it can be fatal without proper and timely response.Cholera has been a global public health challenge since 1817. This acute, diarrhoeal, infection caused by the bacterium The increased threat of cholera should not be overlooked at this time when government and non-governmental organization (NGO) health services are overwhelmed by COVID-19. Ethiopia's Tigray crisis provides a critical example of the heightened risk of cholera posed by the intersection of the pandemic and escalating violence; food and water shortages; displacement; and disruptions to infrastructure and health systems.Both Ethiopia and Sudan are cholera endemic regions, which have experienced several cholera epidemics, including recent outbreaks in 2019, 2020, and 2021 .The conflict in the Tigray region puts 2.3 million people at risk, with immediate needs and limited humanitarian access Early prediction and preparedness are pivotal to avoid tragic cholera outbreaks like the current one in Yemen, which started in 2016, and has caused over two million suspected cases and nearly 4000 deaths Improving living conditions and provision of proper and sustainable WASH services constitutes a long-term approach in infection control. This involves provision of adequate shelter to reduce overcrowding; supplying food, and good quality and quantity of water supply; building sufficient latrines with proper sewage systems; and distributing enough hygiene kits in order to promote personal hygiene behaviours such as hand washing. Health education and raising awareness, supported by meaningful engagement of the affected communities, is also important to enhance resilience, ownership, and uptake of response measures to prevent and control cholera, COVID-19, and other communicable diseases. These measures are emphasised in the Global Task Force on Cholera Control eradication strategy 2030 to reduce cholera death by 90%, and must be prioritised in forced migrant contexts given evidence that attack rates and case fatality rates may be elevated Both authors were involved in the conception and writing of this manuscript.None to declare.