SynthesisBMC primary care2025
Diagnostic accuracy of artificial intelligence compared to family physicians and dermatologists for skin conditions: a systematic review and meta-analysis.
Synthesis in BMC primary care, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
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Abstract
contextArtificial intelligence (AI) technologies are increasingly used for image recognition, especially for skin lesions. Due to what may be long wait times for dermatology appointments, general practitioners (GPs) are the gatekeepers when it comes to skin diseases requiring rapid treatments.
objectiveThis study aims to examine the diagnostic accuracy of AI in diagnosing skin lesions encountered in primary care and to perform a meta-analysis of AI's in diagnostic accuracy for melanoma detection. METHODOLOGY: This systematic review and meta-analysis, conducted according to the 2020 PRISMA guidelines, included diagnostic accuracy studies using any type of AI applied to photographs or dermoscopy images to diagnose skin lesions encountered in primary care settings. The reference standard was dermatologist consensus or histopathological examination. Searches were conducted in PubMed, Web of Science and Cochrane in December 2023. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model.
resultsBetween 2013 and 2023, 382 studies were found and 38 met the inclusion criteria.AI's accuracy was reported as non-inferior or superior to that of dermatologists in 30 studies, while 4 studies reported that AI was less accurate than dermatologists. Similarly, AI's accuracy was reported as non-inferior or superior to that of GPs in 8 studies, and one study indicated that AI was less accurate than GPs. The meta-analysis showed that AI for the diagnosis of melanoma had a pooled sensitivity of 0.86 (95% CI: 0.80-0.90) and a specificity of 0.94 (95% CI: 0.89-0.97). The diagnostic odds ratio was 44.36 (95% CI: 29.28; 67.1), with an AUC of 0.922 for the SROC curve. Of the 38 included studies, 25 were at high risk of bias, primarily due to patient selection. Datasets were frequently not representative of the outpatient population, as malignant conditions were often overestimated.
conclusionAI appears to perform at a similar level to dermatologists, and the same is true when comparing AI to GPs. This is especially true for serious conditions like melanoma, suggesting that AI could be a valuable tool for GPs in improving patient care.
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