{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T01:54:22Z","timestamp":1790733262638,"version":"4.1.0"},"reference-count":40,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T00:00:00Z","timestamp":1713744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:p>Skin cancer mortality rates continue to rise, and survival analysis is increasingly needed to understand who is at risk and what interventions improve outcomes. However, current statistical methods are limited by inability to synthesize multiple data types, such as patient genetics, clinical history, demographics, and pathology and reveal significant multimodal relationships through predictive algorithms. Advances in computing power and data science enabled the rise of artificial intelligence (AI), which synthesizes vast amounts of data and applies algorithms that enable personalized diagnostic approaches. Here, we analyze AI methods used in skin cancer survival analysis, focusing on supervised learning, unsupervised learning, deep learning, and natural language processing. We illustrate strengths and weaknesses of these approaches with examples. Our PubMed search yielded 14 publications meeting inclusion criteria for this scoping review. Most publications focused on melanoma, particularly histopathologic interpretation with deep learning. Such concentration on a single type of skin cancer amid increasing focus on deep learning highlight growing areas for innovation; however, it also demonstrates opportunity for additional analysis that addresses other types of cutaneous malignancies and expands the scope of prognostication to combine both genetic, histopathologic, and clinical data. Moreover, researchers may leverage multiple AI methods for enhanced benefit in analyses. Expanding AI to this arena may enable improved survival analysis, targeted treatments, and outcomes.<\/jats:p>","DOI":"10.3389\/fmed.2024.1243659","type":"journal-article","created":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T04:37:55Z","timestamp":1713760675000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review"],"prefix":"10.3389","volume":"11","author":[{"given":"Celine M.","family":"Schreidah","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily R.","family":"Gordon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oluwaseyi","family":"Adeuyan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caroline","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brigit A.","family":"Lapolla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua A.","family":"Kent","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George Bingham","family":"Reynolds","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lauren M.","family":"Fahmy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunhua","family":"Weng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas P.","family":"Tatonetti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Herbert S.","family":"Chase","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Itsik","family":"Pe\u2019er","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Larisa J.","family":"Geskin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,4,22]]},"reference":[{"key":"ref1","first-page":"591","article-title":"Vital signs: melanoma incidence and mortality trends and projections \u2014 United States, 1982\u20132030","volume":"64","author":"Guy","year":"2015","journal-title":"Morb Mortal Wkly Rep"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.1001\/jamadermatol.2015.1187","article-title":"Incidence estimate of nonmelanoma skin Cancer (keratinocyte carcinomas) in the U.S. population, 2012","volume":"151","author":"Rogers","year":"2015","journal-title":"JAMA Dermatol"},{"key":"ref3","year":"2023"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1001\/jamadermatol.2017.3395","article-title":"The treatment of actinic Keratoses-the rule rather than the exception","volume":"153","author":"Mansouri","year":"2017","journal-title":"JAMA Dermatol"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.3390\/cancers15041183","article-title":"AI-powered diagnosis of skin Cancer: a contemporary review, open challenges and future research directions","volume":"15","author":"Melarkode","year":"2023","journal-title":"Cancers"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"2945","DOI":"10.2147\/IJGM.S340620","article-title":"Incidence trends and survival prediction of malignant skin Cancer: a SEER-based study","volume":"15","author":"Zhu","year":"2022","journal-title":"Int J Gen Med"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1038\/s41416-020-01144-5","article-title":"Understanding the impact of sex and stage differences on melanoma cancer patient survival: a SEER-based study","volume":"124","author":"Smith","year":"2021","journal-title":"Br J Cancer"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1001\/archdermatol.2011.1133","article-title":"Risk and survival of cutaneous melanoma diagnosed subsequent to a previous Cancer","volume":"147","author":"Yang","year":"2011","journal-title":"Arch Dermatol"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"2641","DOI":"10.1007\/s10462-021-10074-4","article-title":"Cancer diagnosis using artificial intelligence: a review","volume":"55","author":"Shastry","year":"2022","journal-title":"Artif Intell Rev"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1016\/j.beth.2020.05.002","article-title":"Supervised machine learning: a brief primer","volume":"51","author":"Jiang","year":"2020","journal-title":"Behav Ther"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.semcancer.2021.04.013","article-title":"Artificial intelligence in oncology: from bench to clinic","volume":"84","author":"Elkhader","year":"2022","journal-title":"Semin Cancer Biol"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"e1002323","DOI":"10.1371\/journal.pcbi.1002323","article-title":"Prediction of drug combinations by integrating molecular and pharmacological data","volume":"7","author":"Zhao","year":"2011","journal-title":"PLoS Comput Biol"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1186\/s12916-019-1368-8","article-title":"The uncertainty with using risk prediction models for individual decision making: an exemplar cohort study examining the prediction of cardiovascular disease in English primary care","volume":"17","author":"Pate","year":"2019","journal-title":"BMC Med"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1038\/s41746-023-00831-w","article-title":"An artificial intelligence based app for skin cancer detection evaluated in a population based setting","volume":"6","author":"Smak Gregoor","year":"2023","journal-title":"NPJ Digit Med"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"1815","DOI":"10.3390\/diagnostics13101815","article-title":"Diagnosing melanomas in Dermoscopy images using deep learning","volume":"13","author":"Alwakid","year":"2023","journal-title":"Diagnostics"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1186\/s13073-016-0339-3","article-title":"The prognostic potential of alternative transcript isoforms across human tumors","volume":"8","author":"Trincado","year":"2016","journal-title":"Genome Med"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"2896","DOI":"10.12659\/MSM.913881","article-title":"Integrative analysis of long noncoding RNA (lncRNA), microRNA (miRNA) and mRNA expression and construction of a competing endogenous RNA (ceRNA) network in metastatic melanoma","volume":"25","author":"Wang","year":"2019","journal-title":"Med Sci Monit Int Med J Exp Clin Res"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1158\/1535-7163.MCT-20-0654","article-title":"PLK1 and NOTCH positively correlate in melanoma and their combined inhibition results in synergistic modulations of key melanoma pathways","volume":"20","author":"Su","year":"2021","journal-title":"Mol Cancer Ther"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.1158\/0008-5472.CAN-19-2268","article-title":"Topological tumor graphs: a graph-based spatial model to infer stromal recruitment for immunosuppression in melanoma histology","volume":"80","author":"Failmezger","year":"2020","journal-title":"Cancer Res"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"102077","DOI":"10.1016\/j.artmed.2021.102077","article-title":"Fenchel duality of cox partial likelihood with an application in survival kernel learning","volume":"116","author":"Wilson","year":"2021","journal-title":"Artif Intell Med"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"1178","DOI":"10.3892\/ijo.2018.4268","article-title":"A six-long non-coding RNA signature predicts prognosis in melanoma patients","volume":"52","author":"Yang","year":"2018","journal-title":"Int J Oncol"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1186\/s12967-018-1632-2","article-title":"Integrative analysis of the cancer genome atlas and cancer cell lines encyclopedia large-scale genomic databases: MUC4\/MUC16\/MUC20 signature is associated with poor survival in human carcinomas","volume":"16","author":"Jonckheere","year":"2018","journal-title":"J Transl Med"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"e0257949","DOI":"10.1371\/journal.pone.0257949","article-title":"Integrating additional factors into the TNM staging for cutaneous melanoma by machine learning","volume":"16","author":"Yang","year":"2021","journal-title":"PLoS One"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"20366","DOI":"10.1038\/s41598-022-24315-1","article-title":"A deep learning model based on whole slide images to predict disease-free survival in cutaneous melanoma patients","volume":"12","author":"Comes","year":"2022","journal-title":"Sci Rep"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1158\/1078-0432.CCR-20-2415","article-title":"Using machine learning algorithms to predict immunotherapy response in patients with advanced melanoma","volume":"27","author":"Johannet","year":"2021","journal-title":"Clin Cancer Res Off J Am Assoc Cancer Res"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"2809","DOI":"10.1038\/s41598-021-82305-1","article-title":"Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma","volume":"11","author":"Moore","year":"2021","journal-title":"Sci Rep"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1038\/s41379-020-00686-6","article-title":"Optimization of an automated tumor-infiltrating lymphocyte algorithm for improved prognostication in primary melanoma","volume":"34","author":"Chou","year":"2021","journal-title":"Mod Pathol Off J U S Can Acad Pathol Inc"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"2144","DOI":"10.1109\/EMBC46164.2021.9630234","article-title":"Deep neural network-based survival analysis for skin Cancer prediction in heart transplant recipients","volume":"2021","author":"Chiu","year":"2021","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Int Conf"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1002\/sim.4780131202","article-title":"Survival analysis and neural nets","volume":"13","author":"Liest\u00f8l","year":"1994","journal-title":"Stat Med"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"101498","DOI":"10.1016\/j.jgo.2023.101498","article-title":"Supervised learning applied to classifying fallers versus non-fallers among older adults with cancer","volume":"14","author":"Ramsdale","year":"2023","journal-title":"J Geriatr Oncol"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1038\/s43856-023-00298-6","article-title":"Unsupervised meta-clustering identifies risk clusters in acute myeloid leukemia based on clinical and genetic profiles","volume":"3","author":"Eckardt","year":"2023","journal-title":"Commun Med"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"274","DOI":"10.4103\/0974-7788.76794","article-title":"Understanding survival analysis: Kaplan-Meier estimate","volume":"1","author":"Goel","year":"2010","journal-title":"Int J Ayurveda Res"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1001\/jamaoncol.2016.0213","article-title":"Natural language processing in oncology: a review. JAMA","volume":"2","author":"Yim","year":"2016","journal-title":"JAMA Oncol"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"e2114723","DOI":"10.1001\/jamanetworkopen.2021.14723","article-title":"Performance of a machine learning algorithm using electronic health record data to identify and estimate survival in a longitudinal cohort of patients with lung Cancer","volume":"4","author":"Yuan","year":"2021","journal-title":"JAMA Netw Open"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"e2126337","DOI":"10.1001\/jamanetworkopen.2021.26337","article-title":"Assessing the prognostic significance of tumor-infiltrating lymphocytes in patients with melanoma using pathologic features identified by natural language processing","volume":"4","author":"Yang","year":"2021","journal-title":"JAMA Netw Open"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"1593","DOI":"10.1038\/s41591-023-02366-9","article-title":"AI-generated text may have a role in evidence-based medicine","volume":"29","author":"Peng","year":"2023","journal-title":"Nat Med"},{"key":"ref38","first-page":"42","article-title":"Racial underrepresentation in dermatological datasets leads to biased machine learning models and inequitable healthcare","volume":"3","author":"Kleinberg","year":"2022","journal-title":"J Biomed Res"},{"key":"ref39","year":"2021"},{"key":"ref40","year":"2023"}],"container-title":["Frontiers in Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2024.1243659\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T04:38:00Z","timestamp":1713760680000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2024.1243659\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,22]]},"references-count":40,"alternative-id":["10.3389\/fmed.2024.1243659"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2024.1243659","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,22]]},"article-number":"1243659"}}