{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T09:47:01Z","timestamp":1785836821127,"version":"3.56.0"},"reference-count":20,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:00:00Z","timestamp":1775088000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:00:00Z","timestamp":1775088000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Deutsche Derma Gesell"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) algorithms have advanced and recently shown high accuracy in diagnosing skin cancer from dermoscopic images. This study compared the diagnostic performance of the large language model ChatGPT\u20104 with that of specialized convolutional neural network (CNN)\u2010based models in analyzing melanocytic lesions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Patients and Methods<\/jats:title>\n                    <jats:p>A cross\u2010sectional comparative study was conducted using 117 dermoscopic images. The performance of ChatGPT\u20104 was assessed under two conditions: diagnosing lesions directly without annotations and diagnosing after annotating dermoscopic features. Results were compared with CNN\u2010based models (YPSONO and ResNet) and human expert evaluations. The confusion matrices of all the models were calculated in addition to the diagnostic accuracy, sensitivity, specificity, and interobserver agreement (Cohen's Kappa).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>ChatGPT\u20104 achieved 92 % sensitivity, 89 % specificity, and an accuracy of 89.7 % in direct diagnosis. When annotations were required, sensitivity and specificity dropped to 68 % and 64 %, respectively. Agreement with experts on dermoscopic patterns was minimal (Cohen's Kappa = 0.13). ChatGPT\u20104 outperformed CNN models in direct diagnosis but exhibited notable limitations in describing dermoscopic features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ChatGPT\u20104 demonstrated promising potential for accurate melanoma versus nevus classification without annotations, surpassing CNN\u2010based models. However, its limited ability to describe dermoscopic features accurately highlights the need for further research and training.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ddg.70039","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T14:19:23Z","timestamp":1775139563000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Beyond black\u2010box AI: Comparing ChatGPT\u20104 interpretability and accuracy to CNNs in melanocytic lesions diagnosis"],"prefix":"10.1111","volume":"24","author":[{"given":"Ofer","family":"Reiter","sequence":"first","affiliation":[{"name":"Division of Dermatology Rabin Medical Center  Petah Tikvah Israel"},{"name":"Faculty of Medicine Tel Aviv University  Tel Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4040-3640","authenticated-orcid":false,"given":"Cristian","family":"Navarrete\u2010Dechent","sequence":"additional","affiliation":[{"name":"Department of Dermatology Pontificia Universidad Cat\u00f3lica de Chile  Santiago Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mor","family":"Atlas","sequence":"additional","affiliation":[{"name":"Ono Academic College, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nir","family":"Nathansohn","sequence":"additional","affiliation":[{"name":"Department of Dermatology Sheba Medical Center  Tel Hashomer Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaron Ben","family":"Mordehai","sequence":"additional","affiliation":[{"name":"Maccabi Healthcare Services  Tel Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomer","family":"Mimouni","sequence":"additional","affiliation":[{"name":"Faculty of Medicine Tel Aviv University  Tel Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8777-8316","authenticated-orcid":false,"given":"Romi","family":"Gleicher","sequence":"additional","affiliation":[{"name":"Bruce and Ruth Rappaport Faculty of Medicine Technion \u2013 Israel Institute of Technology  Haifa Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahdi","family":"Awwad","sequence":"additional","affiliation":[{"name":"Tzafon Medical Center Ophthalmology Unit  Tiberias Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4827-7075","authenticated-orcid":false,"given":"Itay","family":"Cohen","sequence":"additional","affiliation":[{"name":"Rutgers School of Public Health Rutgers University  Piscataway, NJ USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziad","family":"Khamaysi","sequence":"additional","affiliation":[{"name":"Bruce and Ruth Rappaport Faculty of Medicine Technion \u2013 Israel Institute of Technology  Haifa Israel"},{"name":"Department of Dermatology Rambam Health Care Campus  Haifa Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5154-7984","authenticated-orcid":false,"given":"Jonathan","family":"Shapiro","sequence":"additional","affiliation":[{"name":"Maccabi Healthcare Services  Tel Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,2]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(19)30333-X"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamadermatol.2019.1375"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01103-x"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1900654116"},{"key":"e_1_2_7_7_1","unstructured":"NoriH KN McKinneySM CarignanD HorvitzE.Capabilities of GPT\u20104 on Medical Challenge Problems 2023: Available from:http:\/\/arxiv.org\/abs\/2303.13375. 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