{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T09:10:46Z","timestamp":1784797846792,"version":"3.55.0"},"reference-count":8,"publisher":"PAGEPress Publications","license":[{"start":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T00:00:00Z","timestamp":1784764800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Dermatol Reports"],"abstract":"<jats:p>Artificial intelligence (AI)-based dermoscopic analysis is increasingly being used to assist dermatologists in the early detection and monitoring of pigmented skin lesions. However, discrepancies between AI-generated assessments and clinical evaluation may occur. This retrospective study aimed to evaluate the level of agreement between dermatologist assessment and AI-generated analysis in the dermoscopic evaluation of skin lesions. The study included 71 dermoscopic cases examined at two time points between September 2024 and January 2026 at a dermatologic clinic in Tirana, Albania. Dermoscopic images were acquired using a digital dermoscopy platform equipped with an integrated AI algorithm for automated lesion analysis. Agreement between the dermatologist and the AI system was observed in 58 cases (81.7%), whereas 13 cases (18.3%) demonstrated discordance. The 95% confidence interval for the agreement rate was 72.7%-90.7%, indicating a high level of concordance between clinical assessment and AI-based evaluation. These findings suggest that AI-assisted dermoscopic analysis demonstrates substantial agreement with clinical evaluation and may serve as a valuable supportive tool in dermatologic practice. Nevertheless, the observed discordant cases highlight the continuing importance of clinical expertise and established dermoscopic criteria in guiding patient management. AI systems should therefore be regarded as complementary decision-support tools rather than independent diagnostic systems.<\/jats:p>","DOI":"10.4081\/dr.2026.10872","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T08:56:07Z","timestamp":1784796967000},"source":"Crossref","is-referenced-by-count":0,"title":["Comparison between physician assessment and artificial intelligence analysis in digital dermoscopy: evaluation of 71 dermoscopic cases"],"prefix":"10.4081","author":[{"given":"Entela","family":"Shkodrani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alert","family":"Xhaja","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Shameti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barbara","family":"Shkodrani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2549","published-online":{"date-parts":[[2026,7,23]]},"reference":[{"key":"42038","doi-asserted-by":"crossref","unstructured":"1. Marchetti MA, Cowen EA, Kurtansky NR, et al. Prospective validation of dermoscopy-based open-source artificial intelligence for melanoma diagnosis (PROVE-AI study). NPJ Digit Med 2023;6:127.","DOI":"10.1038\/s41746-023-00872-1"},{"key":"42039","doi-asserted-by":"crossref","unstructured":"2. Kittler H, Pehamberger H, Wolff K, Binder M. Diagnostic accuracy of dermoscopy. Lancet Oncol 2002;3:159-65.","DOI":"10.1016\/S1470-2045(02)00679-4"},{"key":"42040","doi-asserted-by":"crossref","unstructured":"3. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115-8. Erratum in: Nature 2017;546:686.","DOI":"10.1038\/nature21056"},{"key":"42041","doi-asserted-by":"crossref","unstructured":"4. Tschandl P, Rosendahl C, Akay BN, et al. Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks. JAMA Dermatol 2019;155:58-65.","DOI":"10.1001\/jamadermatol.2018.4378"},{"key":"42042","doi-asserted-by":"crossref","unstructured":"5. Tschandl P, Rosendahl C, Kittler H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data 2018;5:180161.","DOI":"10.1038\/sdata.2018.161"},{"key":"42043","doi-asserted-by":"crossref","unstructured":"6. Haenssle HA, Fink C, Schneiderbauer R, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol 2018;29:1836-42.","DOI":"10.1093\/annonc\/mdy520"},{"key":"42044","doi-asserted-by":"crossref","unstructured":"7. Celebi ME, Codella N, Halpern A. Dermoscopy Image Analysis: Overview and Future Directions. IEEE J Biomed Health Inform 2019;23:474-8.","DOI":"10.1109\/JBHI.2019.2895803"},{"key":"42045","unstructured":"8. Daneshjou R, Kovarik C, Ko JM. Towards realization of augmented intelligence in dermatology: advances and future directions. NPJ Digit Med 2021."}],"container-title":["Dermatology Reports"],"original-title":[],"link":[{"URL":"https:\/\/journals.pagepress.net\/dr\/article\/download\/10872\/9986","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.pagepress.net\/dr\/article\/download\/10872\/9986","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T08:56:13Z","timestamp":1784796973000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.pagepress.net\/dr\/article\/view\/10872"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,23]]},"references-count":8,"URL":"https:\/\/doi.org\/10.4081\/dr.2026.10872","relation":{},"ISSN":["2036-7406","2036-7392"],"issn-type":[{"value":"2036-7406","type":"electronic"},{"value":"2036-7392","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7,23]]}}}