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Consecutive adults with one or more skin lesions suspicious for skin cancer were randomly allocated to four non-dermatology trainees and four dermatology residents, who examined them with or without real-time help from the algorithm. Among 576 patients with Fitzpatrick skin types III or IV, the physicians' diagnoses were correct in 53.9% of the 295 cases with help and in 43.8% of the 281 cases without (P = 0.019). The gain came from the non-dermatology trainees, whose accuracy was 54.7% with the algorithm and 30.7% without; for the dermatology residents it was not significant. Han is the first author of the trial report.","category":"general benefit","unexpected":false,"purpose":"Model Dermatology was built to suggest diagnoses for skin lesions from photographs. The higher diagnostic accuracy was that purpose.","system":"Model Dermatology","model_version":null,"developer":"Seung Seog Han (I Dermatology Clinic and IDerma, Inc.)","deployer":"Department of Dermatology, Asan Medical Center, Ulsan University College of Medicine","location":"Seoul, South Korea","domain":"Healthcare","segment":null,"benefit_type":"Improved Task Accuracy","taxonomy_note":null,"registry_ids":{},"ratings":[{"group":"personal","depth":2,"reason":"Patients examined with the algorithm received a correct diagnosis in 53.9% of cases against 43.8% without it."},{"group":"organizational","depth":2,"reason":"Non-dermatology trainees diagnosed 54.7% of lesions correctly with the algorithm against 30.7% without it."}],"ai_role":{"status":"assessed","statement":"Patients were randomly assigned to examination with or without the algorithm, so the difference in accuracy is attributable to the algorithm."},"evidence":[{"kind":"peer-reviewed study","citation":"Ang XL, Oh CC. The use of artificial intelligence for skin cancer detection in Asia: a systematic review. Diagnostics (Basel). 2025;15(7):939. doi:10.3390/diagnostics15070939","title":"The Use of Artificial Intelligence for Skin Cancer Detection in Asia-A Systematic Review","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11988623/","date":"2025-04-07","written_by":"independent","quote":"another study by Han et al. [ 40 ] showed that AI assistance mainly benefited non-dermatology trainees with the top accuracy of the AI-assisted group being significantly higher than that of unaided group, but there was no significant change in performance amongst dermatology residents"},{"kind":"peer-reviewed study","citation":"Han SS, Kim YJ, Moon IJ, et al. Evaluation of Artificial Intelligence-Assisted Diagnosis of Skin Neoplasms: A Single-Center, Paralleled, Unmasked, Randomized Controlled Trial. J Invest Dermatol. 2022;142(9):2353-2362.e2. doi:10.1016/j.jid.2022.02.003","title":"Evaluation of Artificial Intelligence-Assisted Diagnosis of Skin Neoplasms: A Single-Center, Paralleled, Unmasked, Randomized Controlled Trial","url":"https://pubmed.ncbi.nlm.nih.gov/35183551/","date":"2022-02-18","written_by":"makers","quote":"the accuracy of the AI-assisted group (n = 295, 53.9%) was found to be significantly higher than those of the unaided group (n = 281, 43.8%; P = 0.019)"},{"kind":"other","citation":"Model Dermatology web service (build 2020). b2020.modelderm.com.","title":"Model Dermatology","url":"https://b2020.modelderm.com/","date":"2026-10-29","written_by":"makers","quote":"A dermatologist, Han Seung Seog has developed and maintained the service."}],"replication":{"produced_by":["Physicians saw the algorithm's suggestions in real time during the examination.","Most of the gain came from physicians with little dermatology experience.","Patients had Fitzpatrick skin types III or IV."],"must_hold":["Users must be non-experts for whom the suggestions add information.","The algorithm must be right often enough; wrong top suggestions lowered trainee accuracy."],"echoes":[]},"paired_incident":null,"headline":"*Model Dermatology* helps trainees diagnose skin lesions in Seoul","metric":"diagnosed 53.9% correctly against 43.8% without it","comparator":{"against":"Examination by the same group of physicians without the algorithm","before":"43.8% diagnostic accuracy","after":"53.9% diagnostic accuracy","basis":"measured","quote":"the accuracy of the AI-assisted group (n = 295, 53.9%) was found to be significantly higher than those of the unaided group (n = 281, 43.8%; P = 0.019)","url":"https://pubmed.ncbi.nlm.nih.gov/35183551/"},"later":null,"value":null,"developer_country":"South Korea","deployer_country":"South Korea","unrated_because":null,"brief_notes":null,"brief":null,"revisions":[{"date":"2026-10-29","change":"Added the case.","source":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11988623/","editor":"AIBD"}],"confidence":"studied","shade":"high","claim_status":"Independently studied","claim_basis":"Sources: one independent peer-reviewed study, the makers' peer-reviewed study and the makers' other source. 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