{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T00:20:15Z","timestamp":1793924415409,"version":"4.8.0"},"reference-count":34,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"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":["Acad Dermatol Venereol"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Basal cell carcinoma (BCC) is the most common skin cancer, requiring an early diagnosis and accurate margin definition to prevent functional and cosmetic complications. Traditional methods using clinical and dermoscopic images (C&amp;D) often rely on biopsies and histology for final validation. Non\u2010invasive techniques like LC\u2010OCT, enabling \u2018digital biopsies\u2019, are promising alternatives, but remain underutilized due to the expertise required. The development of Artificial Intelligence (AI) algorithms is a promising approach to assist dermatologists in their diagnosis and support the broader adoption of such technologies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>We present a real\u2010time AI assistant for BCC diagnosis with LC\u2010OCT, which is, to date, the only real\u2010time AI model across all dermatological imaging modalities. The study aims to quantify the model's effectiveness when used by dermatologists with different levels of expertise and compare its performance with traditional methods and unaided LC\u2010OCT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This multicenter, retrospective study involved 43 dermatologists in a double\u2010rounded quiz on 200 equivocal BCC lesions. Diagnoses were first made on C&amp;D images, then with LC\u2010OCT or AI\u2010assisted LC\u2010OCT in a randomized manner.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>AI\u2010assisted LC\u2010OCT significantly improves dermatologists' diagnostic performance in detecting BCC (+25.8 points in sensitivity and +16.8 points in specificity compared to C&amp;D), particularly benefiting those with less LC\u2010OCT experience, effectively bridging a 2\u2010year gap of expertise. These results highlight the potential for broader clinical adoption through AI assistance and underscore its promise in reducing the need for invasive procedures and improving patient outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These results support a broader adoption of LC\u2010OCT use in clinical practice thanks to AI assistance and underscore its promise in reducing the need for invasive procedures and improving patient outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jdv.70099","type":"journal-article","created":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T14:42:16Z","timestamp":1760884936000},"page":"1059-1068","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["<scp>AI<\/scp>\n                    \u2010assisted basal cell carcinoma diagnosis with\n                    <scp>LC<\/scp>\n                    \u2010\n                    <scp>OCT<\/scp>\n                    : A\u00a0multicentric retrospective study"],"prefix":"10.1111","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6498-5847","authenticated-orcid":false,"given":"S\u00e9bastien","family":"Fischman","sequence":"first","affiliation":[{"name":"Damae Medical  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Th\u00e9o","family":"Viel","sequence":"additional","affiliation":[{"name":"Damae Medical  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean\u2010Luc","family":"Perrot","sequence":"additional","affiliation":[{"name":"Department of Dermatology University Hospital of Saint\u2010Etienne  Saint\u2010Etienne France"},{"name":"UMR CNRS 5516 Laboratoire Hubert Curien  Saint\u2010Etienne 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