Artificial intelligence (AI) has the potential to transform medical research by improving disease diagnosis, clinical decision-making, and outcome prediction. Despite the rapid adoption of AI and machine learning (ML) in other domains and industry, deployment in medical research and clinical practice poses several challenges due to the inherent characteristics and barriers of the healthcare sector. Therefore, researchers aiming to perform AI-intensive studies require a fundamental understanding of the key concepts, biases, and clinical safety concerns associated with the use of AI. Through the analysis of large, multimodal datasets, AI has the potential to revolutionize orthopaedic research, with new insights regarding the optimal diagnosis and management of patients affected musculoskeletal injury and disease. The article is the first in a series introducing fundamental concepts and best practices to guide healthcare professionals and researcher interested in performing AI-intensive orthopaedic research studies. The vast potential of AI in orthopaedics is illustrated through examples involving disease- or injury-specific outcome prediction, medical image analysis, clinical decision support systems and digital twin technology. Furthermore, it is essential to address the role of human involvement in training unbiased, generalizable AI models, their explainability in high-risk clinical settings and the implementation of expert oversight and clinical safety measures for failure. In conclusion, the opportunities and challenges of AI in medicine are presented to ensure the safe and ethical deployment of AI models for orthopaedic research and clinical application.Level of evidence IV However, increasingly sophisticated applications in engineering, business, and industrial sectors have shown the rapid technological advancement and maturity of AI, with a growing interest for implementation in medical research, and the healthcare sector [Artificial intelligence (AI) is set to transform the landscape of medical research with innovative approaches to improve disease detection, clinical decision-making, and outcome prediction. The majority of medical research conducted throughout 20e sector , 37. Acce sector , 31. TheIn recent years, the growing availability of healthcare data and the increasing maturity of AI as a technological tool initiated a gradual transformation of the medical research landscape. Patient registries containing granular information about the demographics and therapeutic interventions of numerous patient populations present new avenues for research in the age of big data. Electronic medical records permit the storage and traceability of data collected over the entire duration of medical treatment for patients with different medical conditions, including patient history, physical examination results, diagnostic images, interventions and outcomes over time.Artificial intelligence has the potential to revolutionize medical research by enabling rapid and accurate analysis of vast amounts of data, containing demographic, genetic, clinical, surgical, and rehabilitation-specific information or a combination of these from thousands of patients, in pursuit of patterns associated with specific diseases or conditions. Furthermore, many AI systems possess the advantage of the ability to detect patterns, trends and connections that may not be easily recognized by humans, potentially leading to new clinical insights and breakthroughs in disease prevention, diagnostics and treatment. Analysis of large datasets, often with multimodal data content , would be tedious and inefficient with the statistical methods currently employed in medical research . AnotherApplications of AI can be useful in a broad range of research scenarios with far-reaching potential for clinical utility. The aim of this section is to provide the reader with a broad overview of areas with vast potential in orthopaedics using existing examples from AI-intensive medical research.The continuous growth in the availability of high-quality medical data presents new avenues in the analysis of information derived from the results of clinical trials and national patient registries , 18. OneWhile the clinical implementation of AI-driven predictive algorithms is still in its nascency, their potential is demonstrated by several use cases in the current literature. One notable example is a clinical calculator for ACL revision risk prediction, developed with ML models applied to data from the Norwegian Knee Ligament Registry , 28. WhiImage analysis is perhaps the most well-known application of AI in medicine. The ability of ML algorithms to perform classification and pattern recognition tasks when trained on radiographic images led to the proposal of numerous useful applications across fields, such as histopathology, dermatology, cardiology, ophthalmology, and radiology. Promising applications of AI and imaging technologies in these fields include the detection and grading of prostate cancer based on digitalized prostate biopsy slides , automatIn orthopaedics, AI-based image analysis applications have primarily made an impact on diagnostics, surgical planning, and implant design in traumatology, arthroplasty, and spine surgery. While similar approaches are currently underutilized in sports medicine, momentum is increasing in imaging applications for soft-tissue injury detection. A recent study demonstrated excellent diagnostic performance of an ACL tear detection ML algorithm trained on approximately 20,000 magnetic resonance images (MRI), with similar success after external validation on patient groups from two different countries . SimilarThe broad categories and types of data and ML models have led to advances in the implementation of multimodal AI systems . DespiteCurrently, evaluation of the efficiency and efficacy of medical interventions relies on time-consuming clinical trials, registry studies, and small-scale clinical investigations. While the results of clinical trials are considered the gold-standard of evidence synthesis, the clinical benefit of certain medical interventions may vary among individuals in a population. The digital twin is a concept adopted from engineering, and consists of a virtual representation of a real-world physical entity, such as an object, a system, or a patient . The intThe European AI Act, established in 2022, proposes a risk-based approach to the regulation of AI systems, and characterizes medical applications to be of high-risk [In the context of AI, provenance refers to the origins and history of a particular dataset or model. Provenance comprises information about how the data was collected, who collected it, where it was collected, and any transformations or modifications that were applied to it. Provenance is important in AI because it can help ensure that the data and models being used are reliable, trustworthy and can also help identify potential biases or errors in the data. Provenance in AI-based medical research is essential to build the trust required for clinical implementation of decision support systems and prediction tools, and to enable the design of replicable and transparent studies using AI. A hypothetical clinical decision support system designed to help clinicians optimize the treatment of patients with anterior cruciate ligament (ACL) injury can serve as an example to illustrate the role of provenance. Research studies for testing the validity of such a system will need to disclose the origin of the data the AI model was trained on, including the characteristics of the population, the types of variables collected, the timeframe of data collection, potential sources of bias, to name a few. Furthermore, the decision support system will require a detailed description of the data processing pipeline, model selection process, statistical analysis, methods applied to train, test, and validate models, as well as the parameters used to fine-tune the decision support system. Another important step is to disclose metrics used for the assessment of model performance. While seemingly a tedious task, ensuring provenance is necessary to meet the high standards required for the safe and reliable implementation of AI-driven medical research.One of the major concerns with the ability of AI systems to predict events is that steps taken by certain models to reach predictions are often inaccessible. This characteristic, termed black-box decision making, results in an inability for human observers to explain model output in terms of the original input variables. This feature is particularly problematic for medical applications, as current decision making-systems are based on empirical rules, which allow human interpreters to trace the logic behind reasoning that leads to a certain outcome. This currently accepted and transparent approach enables humans to learn from systems, and perhaps as importantly, to detect and rectify errors and biases in the system, which may otherwise lead to false conclusions and even dangerous consequences. While methods have been proposed to improve the explainability of ML models, their implementation may not be feasible with all data types. Consequently, future AI-intensive medical research should focus on enhanced human interpretability, with the conversion of insight provided by the model to tangible knowledge that mirrors those of medical experts, with potential avenues for error detection. White-box ML models, aptly named to show the contrast to black-box models, provide a broken-down explanation of the steps taken to reach a conclusion with insights about how the input data was used throughout the decision process . This feAs previously discussed, training AI systems on high-quality datasets is a major requirement for clinical adaptation. However, even models trained with the most attention to detail and with carefully curated data may not be universally applicable to every clinical setting. What happens when AI systems encounter unexpected changes in clinical context? Some examples of this phenomenon may be obvious, such as the erroneous prediction of ACL rerupture risk in female downhill skiers by a system that was trained predominantly on male football players. However, a more subtle example may be the poor reproducibility of ACL rerupture risk prediction in patients from one country based on a model trained on registry data from another, with different demographics, injury profiles, and surgical techniques. The inability of AI systems to adapt to new situations, termed distributional shift, is a central problem for the universal application of models across different settings, and may be influenced by countless forms of selection bias that are difficult for researchers to foresee . Recent Recent developments towards standardizing the reports of AI-intensive research include the CONSORT-AI , STARD-AWhile the deployment of AI systems opens exciting possibilities in medical research, mitigation of the potential risks of false predictions will be an essential task in the ensuing years. Navigation between models that produce truthful versus misleading outputs may present unique challenges. An important question is the role of human involvement in the training phase of models used in AI systems. While medical research is heavily rooted in evidence-based thinking and expert consensus, it is also prone to human error and bias. Consequently, excessive human supervision in AI-driven research may force AI systems to make errors akin to those made by human reasoning. However, it is also clear that black-box models preclude the explainability required for the implementation of AI systems in high-risk clinical settings . While cThis presents an important dilemma with practical and philosophical implications. One approach to solving complex research questions is to entrust models built on ground truths founded on human clinical knowledge and existing evidence. The advantage of such supervised learning is that truths are derived using representations comprehensible to humans, which in turn allows human assessment for correctness. Alternatively, certain models are capable of a more intuitive approach, with ground truths based on implicitly derived knowledge by the model, without human supervision of the learning process. In turn, an unsupervised learning approach can provide the benefit of superior pattern recognition and complex, intuitive reasoning at the cost of human interpretability and assessment of the clinical relevance in the underlying logic. Future research will be required to reconcile supervised and unsupervised approaches in medical AI system development, and to ensure explainability and truthfulness .While the recent application of large language models, such as ChatGPT by Open AI , 35 and A fundamental technical introduction to AI and ML for orthopaedic researchers, with a focus on the potential approaches to be used in medical research.Familiarity with the current state of AI in medical research and understanding of the potential benefit conferred by AI in orthopaedics.Approaching hypotheses and research questions in orthopaedic research using AI methods and requirements for interdisciplinary collaboration.Data management for AI-driven orthopaedic research projects.Understanding and interpreting the output of ML-models and AI systems.End-product verification, safety in clinical use, and regulatory concerns.A comprehensive checklist with regards to the previous principles to guide implementation of AI-driven research in orthopaedics.The boundaries of the safe and ethical use of AI in orthopaedic research remain to be determined. In the long-term, over-reliance on AI-driven algorithmic diagnosis, risk-prediction, and prognostics may erode the critical thinking skills considered so essential for clinical medical practice today. Similar to the broad range of industries and scientific domains, careful planning will likely be required to strike the appropriate balance between human- and AI-driven innovation in orthopaedics and sports medicine. While AI will likely exceed human performance in areas such as data analysis, pattern recognition, and decision-making, the goal of clinicians and researchers will be to identify and execute innovative AI-driven applications in medicine and thereby enhance the quality of patient care. The aim of subsequent parts of this learning series is to supply readers with the competence to design and implement AI-driven research projects through proficiency in the following topics:


This study aims to report analyses regarding the global distribution ofinstitutions involved in clinical trials of COVID-19 vaccines throughoutFebruary 2022. We retrieved global data from the World Health Organizationreport on vaccine development. These data allowed us to identify projectinstitutions and plot their geographic coordinates. We produced a georeferencedmap using an R programming environment and, based on the geographical locationof vaccine developers, we analyzed the subcontinental distribution of clinicaltrials and the nature of the vaccines. Regionally, South-Southeast Asiancountries carried out more clinical trials than any other region,proportionally, although this happened solely for mature technologies. Fewtrials were under implementation in Latin America and Africa. Our findingsconfirm previous studies on the regional concentration in the development oftechnology. However, our contribution lies in showing these phenomena forCOVID-19 vaccines in specific subcontinents and technologies, at a countrylevel. Our data underscores which subcontinents perform very few clinical trialsfor COVID-19 and seem to be ill-prepared for future disease outbreaks, and ifthese become epidemics or even pandemics and require domestic vaccinedevelopment or production. We also consider the case of Brazil, which did notfinish the complete cycle of COVID-19 vaccine development in the indicatedperiod; but, with favorable policies, it has potential to engage further inCOVID-19 vaccine technology.  Efforts for the development of a vaccine for COVID-19 gained momentum when the WorldHealth Organization (WHO) declared a global pandemic on March 11th, 2020 ,,Public agencies, public research institutes, universities, and private organizationsestablished partnerships to develop vaccines. Alliances and cooperation among theseagents allowed for the articulation of complementary technical capacities, e.g., inthe field of Biotechnology Evidence showed that vaccine development took place in record time, but also in anunequal manner among different countries and occurred predominantly in developedcountries The effective control of the pandemic requires improved regional alignment andinvolvement of the Global South in vaccine research and development, especiallyconsidering pandemic epicenters in countries whose capacity for vaccine developmentis relatively limited  We base our analysis on the point of view that vaccine development is a broadprocess, in wich many complex and complementary stages are required to accomplishnovel pharmaceutical innovation All institutions listed as vaccines developers by the WHO and involved in theclinical trials of COVID-19 vaccine development were tabulated based on data fromthe WHO report COVID - Landscape of novel coronavirus candidate vaccine developmentworldwide http://www.r-project.org).The distribution of vaccine development by country shown in ,,,Our findings are consistent with studies on the globalized development of technology,which indicates a trend toward the development of technologies in specific regionsWe identified a concentration of vaccine developers in North America, Western Europe,and South-Southeast Asia. Hence, technological activities are regionallyconcentrated but fragmented in different countries within these regions. Inqualitative terms, Ghattas et al. We note the secondary role of Brazil compared to Russia, India, China, and Cuba.Besides, Latin America and Africa present extremely low figures regarding clinicaltrials. Our data suggest the absence of clinical trials in Eastern Europe. Thisunderscores the need for such activities in these regions in order to respond tofuture pandemics. International efforts in vaccine development could be betteraligned, so that regions lagging begin to take part in clinical trials. Brazil’ssecondary position and its efforts to respond to COVID-19 This study provides an overview of the global distribution of the development ofvaccines in the clinical trial phase for COVID-19. The results are coherent withobservations made by Thanh Le et al. Our contribution consisted in the identification of the subcontinents and countriesin leading positions regarding the different technologies tested and describing thepattern of this phenomenon. The findings show that the COVID-19 vaccine developmentprojects in the clinical trial phase are concentrated in South-Southeast Asia(mature technologies), followed by North America (modern technologies), and WesternEurope (modern technologies). An accentuated fragmentation is found at the nationallevel in the latter two regions. These results confirm the findings of previousstudies on the relative concentration of the international distribution oftechnological development. This trend shows a global scenario of considerableinequality in terms of improvements in public health for subcontinents withrelatively limited capacity to respond to future pandemics. Clinical trials shouldbe intensified, integrating regions excluded from testing activities - this callsfor increased regional alignment in vaccine development. ,Helsinki Declaration.Participation in such studies show, to international partners, the existence oflocal expertise in this domain We recognize the development of active pharmaceutical ingredients and manufacturingprocess adaptation and learning as equally relevant and complex when compared toclinical trials. We also acknowledge that an intricate supply chain was articulatedglobally and in record time, with drug substance being a key input manufactured by afew producers in an oligopolistic fashion Weak participation in clinical trials is noted in both Latin America and Africa, witha virtual absence of projects in Eastern Europe. One should reflect on the impact ofCOVID-19 in these regions, considering the dependence on the supply of immunizingagents by private companies and foreign governments. In opposition to suchdependence, engaging actively in vaccine development and production could be abetter way to face the effects of future disease outbreaks, epidemics, and evenpandemics.