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Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The development of diagnostic tools for skin cancer based on artificial intelligence (AI) is increasing rapidly and will likely soon be widely implemented in clinical use. Even though the performance of these algorithms is promising in theory, there is limited evidence on the impact of AI assistance on human diagnostic decisions. Therefore, the aim of this systematic review and meta-analysis was to study the effect of AI assistance on the accuracy of skin cancer diagnosis. We searched PubMed, Embase, IEE Xplore, Scopus and conference proceedings for articles from 1\/1\/2017 to 11\/8\/2022. We included studies comparing the performance of clinicians diagnosing at least one skin cancer with and without deep learning-based AI assistance. Summary estimates of sensitivity and specificity of diagnostic accuracy with versus without AI assistance were computed using a bivariate random effects model. We identified 2983 studies, of which ten were eligible for meta-analysis. For clinicians without AI assistance, pooled sensitivity was 74.8% (95% CI 68.6\u201380.1) and specificity was 81.5% (95% CI 73.9\u201387.3). For AI-assisted clinicians, the overall sensitivity was 81.1% (95% CI 74.4\u201386.5) and specificity was 86.1% (95% CI 79.2\u201390.9). AI benefitted medical professionals of all experience levels in subgroup analyses, with the largest improvement among non-dermatologists. No publication bias was detected, and sensitivity analysis revealed that the findings were robust. AI in the hands of clinicians has the potential to improve diagnostic accuracy in skin cancer diagnosis. Given that most studies were conducted in experimental settings, we encourage future studies to further investigate these potential benefits in real-life settings.<\/jats:p>","DOI":"10.1038\/s41746-024-01031-w","type":"journal-article","created":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T10:02:06Z","timestamp":1712656926000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Human-AI interaction in skin cancer diagnosis: a systematic review and meta-analysis"],"prefix":"10.1038","volume":"7","author":[{"given":"Isabelle","family":"Krakowski","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2869-5751","authenticated-orcid":false,"given":"Jiyeong","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Zhuo Ran","family":"Cai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7988-9356","authenticated-orcid":false,"given":"Roxana","family":"Daneshjou","sequence":"additional","affiliation":[]},{"given":"Jan","family":"Lapins","sequence":"additional","affiliation":[]},{"given":"Hanna","family":"Eriksson","sequence":"additional","affiliation":[]},{"given":"Anastasia","family":"Lykou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5856-6301","authenticated-orcid":false,"given":"Eleni","family":"Linos","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,9]]},"reference":[{"key":"1031_CR1","doi-asserted-by":"publisher","first-page":"1530","DOI":"10.1126\/science.aap8062","volume":"358","author":"E Brynjolfsson","year":"2017","unstructured":"Brynjolfsson, E. & Mitchell, T. 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RD is an AAD AI committee member and Associate Editor at the Journal of Investigative Dermatology, has received consulting fees from Pfizer, L\u2019Oreal, Frazier Healthcare Partners, and has stock options in Revea and MDAlgorithms. All other authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"78"}}