A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis

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dc.catalogadorpau
dc.contributor.authorSalinas, María Paz
dc.contributor.authorSepúlveda, Javiera
dc.contributor.authorHidalgo, Leonel
dc.contributor.authorPeirano, Dominga
dc.contributor.authorMorel, Macarena
dc.contributor.authorUribe, Pablo
dc.contributor.authorRotemberg, Verónica
dc.contributor.authorBriones, Juan
dc.contributor.authorMery, Domingo
dc.contributor.authorNavarrete-Dechent, Cristian
dc.date.accessioned2024-05-27T15:04:37Z
dc.date.available2024-05-27T15:04:37Z
dc.date.issued2024
dc.description.abstractScientific research of artificial intelligence (AI) in dermatology has increased exponentially. The objective of this study was to perform a systematic review and meta-analysis to evaluate the performance of AI algorithms for skin cancer classification in comparison to clinicians with different levels of expertise. Based on PRISMA guidelines, 3 electronic databases (PubMed, Embase, and Cochrane Library) were screened for relevant articles up to August 2022. The quality of the studies was assessed using QUADAS-2. A meta-analysis of sensitivity and specificity was performed for the accuracy of AI and clinicians. Fifty-three studies were included in the systematic review, and 19 met the inclusion criteria for the meta-analysis. Considering all studies and all subgroups of clinicians, we found a sensitivity (Sn) and specificity (Sp) of 87.0% and 77.1% for AI algorithms, respectively, and a Sn of 79.78% and Sp of 73.6% for all clinicians (overall); differences were statistically significant for both Sn and Sp. The difference between AI performance (Sn 92.5%, Sp 66.5%) vs. generalists (Sn 64.6%, Sp 72.8%), was greater, when compared with expert clinicians. Performance between AI algorithms (Sn 86.3%, Sp 78.4%) vs expert dermatologists (Sn 84.2%, Sp 74.4%) was clinically comparable. Limitations of AI algorithms in clinical practice should be considered, and future studies should focus on real-world settings, and towards AI-assistance.
dc.description.funderANID - Millennium Science Initiative
dc.description.sponsorshipLa Fondation La Roche Possay Research Awards. ANID - Millennium Science Initiative Program ICN2021_004
dc.fechaingreso.objetodigital2024-05-24
dc.fuente.origenORCID
dc.identifier.doi10.1038/s41746-024-01103-x
dc.identifier.urihttps://doi.org/10.1038/s41746-024-01103-x
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/85801
dc.identifier.wosidWOS:001222621300002
dc.information.autorucEscuela de Medicina; Navarrete Dechent, Cristian Patricio; 0000-0003-4040-3640; 156251
dc.information.autorucEscuela de Medicina; Salinas, María Paz; 0000-0001-5610-988X; 1147825
dc.issue.numero7
dc.language.isoen
dc.nota.accesocontenido completo
dc.rightsacceso abierto
dc.subject.ddc610
dc.subject.deweyMedicina y saludes_ES
dc.subject.ods03 Good health and well-being
dc.subject.odspa03 Salud y bienestar
dc.titleA systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis
dc.typeartículo
dc.volumen125
sipa.codpersvinculados156251
sipa.codpersvinculados1147825
sipa.trazabilidadORCID;2024-05-20
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