{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T05:56:04Z","timestamp":1792821364684,"version":"4.5.0"},"reference-count":24,"publisher":"American Medical Association (AMA)","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Dermatol"],"published-print":{"date-parts":[[2025,8,1]]},"abstract":"Importance<\/jats:title>There exists substantial heterogeneity in outcomes within T stages for patients with cutaneous squamous cell carcinoma (cSCC).<\/jats:p><\/jats:sec>Objective<\/jats:title>To determine whether a customized generative pretrained transformer model, trained on a comprehensive dataset with more than 1 trillion parameters and equipped with relevant focused context and retrieval augmented generation (RAG), could excel in aggregating and interpreting vast quantities of data to develop a novel class-based risk stratification system that outperforms the current standards.<\/jats:p><\/jats:sec>Design, Setting, and Participants<\/jats:title>To build the RAG knowledge base, a systematic review of the literature was conducted that addressed risk factors for poor outcomes in cSCC. Using the RAG-enabled generative pretrained transformer (GPT) model, we developed a novel class-based risk stratification system that assigned point values for risk factors, culminating in a GPT-based prognostication system called the artificial intelligence\u2013derived risk score (AIRIS). The system\u2019s performance was validated on a combined prospective and retrospective cohort of 2379 primary cSCC tumors (1996-2023) with at least 36 months of follow-up, against Brigham and Women\u2019s Hospital (BWH) and American Joint Committee on Cancer Staging Manual, eighth edition<\/jats:italic> (AJCC8<\/jats:italic>) systems in stratifying risk for locoregional recurrence (LR), nodal metastasis (NM), distant metastasis (DM), and disease-specific death (DSD).<\/jats:p><\/jats:sec>Main Outcomes and Measures<\/jats:title>Performance metrics evaluated included distinctiveness, homogeneity, and monotonicity, as defined by the AJCC8<\/jats:italic>, as well as sensitivity, specificity, positive predictive value, negative predictive value, accuracy, the area under the receiver operating characteristic curve, and concordance.<\/jats:p><\/jats:sec>Results<\/jats:title>The median age at diagnosis was 73 (IQR, 64-81) years, with 38.5% female patients and 61.5% male patients. The AIRIS prognostication system demonstrated superior sensitivity across all outcomes (LR, 49.1%; NM, 73.7%; DM, 82.5%; and DSD, 72.2%) and the highest area under the receiver operating characteristic curve values (LR, 0.69; NM, 0.81; DM, 0.85; and DSD, 0.80), indicating significantly enhanced discriminative capability compared with the BWH and AJCC8<\/jats:italic> systems. While all systems were comparably distinctive, the AIRIS prognostication system consistently demonstrated the lowest proportion of tumors exhibiting poor outcomes in low-risk categories, suggesting its improved homogeneity and monotonicity.<\/jats:p><\/jats:sec>Conclusions and Relevance<\/jats:title>The results of this diagnostic study suggest that the AIRIS system outperforms the existing BWH and AJCC8<\/jats:italic> prognostication systems, potentially providing a more effective tool for predicting poor outcomes in cSCC. This study illustrates the potential of large language models in refining prognostic tools, offering implications for treating patients with cancer.<\/jats:p><\/jats:sec>","DOI":"10.1001\/jamadermatol.2025.1614","type":"journal-article","created":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T11:00:56Z","timestamp":1749639656000},"page":"796","source":"Crossref","is-referenced-by-count":8,"title":["Retrieval Augmented Generation\u2013Enabled Large Language Model for Risk Stratification of Cutaneous Squamous Cell Carcinoma"],"prefix":"10.1001","volume":"161","author":[{"given":"Neil K.","family":"Jairath","sequence":"first","affiliation":[{"name":"The Ronald O. Perelman Department of Dermatology, New York University Grossman School of Medicine, New York"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vartan","family":"Pahalyants","sequence":"additional","affiliation":[{"name":"The Ronald O. 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Perelman Department of Dermatology, New York University Grossman School of Medicine, New York"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zachary","family":"Leibovit-Reiben","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Mayo Clinic Comprehensive Cancer Center, Phoenix, Arizona"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alyssa","family":"Stockard","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Mayo Clinic Comprehensive Cancer Center, Phoenix, Arizona"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicole","family":"Doudican","sequence":"additional","affiliation":[{"name":"The Ronald O. 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