Accuracy of computer diagnosis of XXXX : a quantitative meta-analysis.

@entity1 @entity320
BACKGROUND: Recent developments in computer technology have raised expectations that fully automated diagnostic instruments will become available to diagnose @entity320 without the need of @entity1 expertise. OBJECTIVES: To critically review the contemporary literature on computer diagnosis of @entity320 , evaluate the accuracy of such computer diagnosis, analyze the influence of study characteristics, and compare the accuracy of computer diagnosis of @entity320 with @entity1 diagnosis. METHODS: Quantitative meta-analysis of published reports. DATA SOURCES: Eligible studies were identified by a MEDLINE search covering the period from January 1991 to March 2002, by manual searches of the reference lists of retrieved articles, and by direct communication with experts. RESULTS: Thirty studies with substantial differences in methodological quality were deemed eligible for meta-analysis. Five of these complied with the predetermined list of "good quality" requirements, but none met all methodological quality requirements. Ten of these studies compared the performance of computer diagnosis with @entity1 diagnosis. The diagnostic accuracy achieved with computer diagnosis was statistically not different from that of @entity1 diagnosis (log odds ratios, 3.36 vs 3.51; P =.80). The diagnostic performance of the computer diagnosis was better for studies that used dermoscopic images than for studies that used clinical images (log odds ratios, 4.2 vs 3.4; P =.08). Other study characteristics did not significantly influence the accuracy of the computer diagnosis. CONCLUSIONS: The computer diagnosis of @entity320 is accurate under experimental conditions, but the practical value of automated diagnostic instruments under real-world conditions is currently unknown. We suggest minimum requirements for methodological quality in future experimental studies or, ideally, randomized controlled trials.
