{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T05:49:51Z","timestamp":1776404991798,"version":"3.51.2"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T00:00:00Z","timestamp":1766361600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:00:00Z","timestamp":1766966400000},"content-version":"vor","delay-in-days":7,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["P30 CA008748"],"award-info":[{"award-number":["P30 CA008748"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Cancer Institute,United States","award":["U24 CA264369"],"award-info":[{"award-number":["U24 CA264369"]}]},{"DOI":"10.13039\/100001299","name":"Prevent Cancer Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001299","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000861","name":"Burroughs Wellcome Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000861","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Skin tone affects artificial intelligence (AI) performance in dermatology. While labeling datasets for skin tone could improve algorithm generalizability for detecting dermatologic malignancies, large-scale validation of skin tone assessments is lacking. This prospective observational study assessed reliability of subjective tools (Fitzpatrick Skin Type [FST], Monk Skin Tone [MST], Pantone SkinTone Guide) and an objective colorimeter for in-person and photography-based settings to evaluate utility for labeling dermoscopic datasets. Colorimetry (gold standard for color measurement) demonstrated high precision with in-person measurements. Of subjective scales, MST demonstrated slightly tighter clustering in the color space and high repeatability for in-person and photography-based assessments (latter varied by lighting). Dermoscopic image-extracted color values correlated poorly with colorimetry values. For subjective ratings, MST more effectively captured differences in AI melanoma classification scores than FST. Findings underscore that FST is not a proxy for skin tone; an important role remains for skin tone assessment to improve AI performance.<\/jats:p>","DOI":"10.1038\/s41746-025-02245-2","type":"journal-article","created":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T15:54:12Z","timestamp":1766418852000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Evaluating skin tone scales for dermatologic dataset labeling: a prospective-comparative study"],"prefix":"10.1038","volume":"8","author":[{"given":"Vanessa R.","family":"Weir","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingjoy","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maura C.","family":"Gillis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicholas R.","family":"Kurtansky","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Trina","family":"Salvador","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Allan C.","family":"Halpern","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kelly C.","family":"Nelson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jenna C.","family":"Lester","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Veronica","family":"Rotemberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,22]]},"reference":[{"key":"2245_CR1","doi-asserted-by":"publisher","first-page":"1229","DOI":"10.1038\/s41591-020-0942-0","volume":"26","author":"P Tschandl","year":"2020","unstructured":"Tschandl, P. et al. Human\u2013computer collaboration for skin cancer recognition. Nat. Med. 26, 1229\u20131234 (2020).","journal-title":"Nat. Med."},{"key":"2245_CR2","doi-asserted-by":"publisher","first-page":"1836","DOI":"10.1093\/annonc\/mdy166","volume":"29","author":"HA Haenssle","year":"2018","unstructured":"Haenssle, H. A. 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. J. Eur. Soc. Med. Oncol. 29, 1836\u20131842 (2018).","journal-title":"Ann. Oncol. J. Eur. Soc. Med. Oncol."},{"key":"2245_CR3","doi-asserted-by":"publisher","first-page":"1362","DOI":"10.1001\/jamadermatol.2021.3129","volume":"157","author":"R Daneshjou","year":"2021","unstructured":"Daneshjou, R., Smith, M. P., Sun, M. D., Rotemberg, V. & Zou, J. Lack of transparency and potential bias in artificial intelligence data sets and algorithms: a scoping review. JAMA Dermatol. 157, 1362\u20131369 (2021).","journal-title":"JAMA Dermatol."},{"key":"2245_CR4","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.abq6147","volume":"8","author":"R Daneshjou","year":"2022","unstructured":"Daneshjou, R. et al. Disparities in dermatology AI performance on a diverse, curated clinical image set. Sci. Adv. 8, eabq6147 (2022).","journal-title":"Sci. Adv."},{"key":"2245_CR5","doi-asserted-by":"publisher","unstructured":"Pope, J. et al. Skin cancer machine learning model tone bias. Preprint at https:\/\/doi.org\/10.48550\/ARXIV.2410.06385 (2024).","DOI":"10.48550\/ARXIV.2410.06385"},{"key":"2245_CR6","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1038\/s41591-023-02728-3","volume":"30","author":"M Groh","year":"2024","unstructured":"Groh, M. et al. Deep learning-aided decision support for diagnosis of skin disease across skin tones. Nat. Med. 30, 573\u2013583 (2024).","journal-title":"Nat. Med."},{"key":"2245_CR7","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1001\/archderm.1988.01670060015008","volume":"124","author":"TB Fitzpatrick","year":"1988","unstructured":"Fitzpatrick, T. B. The validity and practicality of sun-reactive skin types I through VI. Arch. Dermatol. 124, 869\u2013871 (1988).","journal-title":"Arch. Dermatol."},{"key":"2245_CR8","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1111\/php.13562","volume":"98","author":"M Osto","year":"2022","unstructured":"Osto, M., Hamzavi, I. H., Lim, H. W. & Kohli, I. Individual typology angle and Fitzpatrick skin phototypes are not equivalent in photodermatology. Photochem. Photobiol. 98, 127\u2013129 (2022).","journal-title":"Photochem. Photobiol."},{"key":"2245_CR9","doi-asserted-by":"publisher","unstructured":"Monk, E. The monk skin tone scale. Preprint at https:\/\/doi.org\/10.31235\/osf.io\/pdf4c (2023).","DOI":"10.31235\/osf.io\/pdf4c"},{"key":"2245_CR10","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1038\/s41746-024-01176-8","volume":"7","author":"VR Weir","year":"2024","unstructured":"Weir, V. R., Dempsey, K., Gichoya, J. W., Rotemberg, V. & Wong, A.-K. I. A survey of skin tone assessment in prospective research. Npj Digit. Med. 7, 191 (2024).","journal-title":"Npj Digit. Med."},{"key":"2245_CR11","doi-asserted-by":"publisher","unstructured":"Schumann, C. et al. Consensus and subjectivity of skin tone annotation for ML fairness. Preprint at https:\/\/doi.org\/10.48550\/ARXIV.2305.09073 (2023).","DOI":"10.48550\/ARXIV.2305.09073"},{"key":"2245_CR12","unstructured":"Pantone. Pantone skintone guide. Pantone https:\/\/www.pantone.com\/skintone (2023)."},{"key":"2245_CR13","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1097\/ASW.0000000000000043","volume":"36","author":"SE Sonenblum","year":"2023","unstructured":"Sonenblum, S. E., Patel, R., Phrasavath, S., Xu, S. & Bates-Jensen, B. M. Using technology to detect erythema across skin tones. Adv. Ski. Wound Care 36, 524\u2013533 (2023).","journal-title":"Adv. Ski. Wound Care"},{"key":"2245_CR14","doi-asserted-by":"publisher","first-page":"1633","DOI":"10.1038\/sj.jid.5701238","volume":"128","author":"LK Pershing","year":"2008","unstructured":"Pershing, L. K. et al. Reflectance spectrophotometer: the dermatologists\u2019 sphygmomanometer for skin phototyping? J. Invest. Dermatol. 128, 1633\u20131640 (2008).","journal-title":"J. Invest. Dermatol."},{"key":"2245_CR15","unstructured":"SkinColorCatch. Delfin Technologies https:\/\/delfintech.com\/products\/skincolorcatch\/ (2019)."},{"key":"2245_CR16","unstructured":"Ha, Q. et al. SIIM\/ISIC 2020 Challenge Winning Algorithm (All Data Are Ext). GitHub repository, ISIC-Research\/ADAE; accessed 1 Jul 2025. https:\/\/github.com\/ISIC-Research\/ADAE (2023)."},{"key":"2245_CR17","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1038\/s41746-023-00872-1","volume":"6","author":"MA Marchetti","year":"2023","unstructured":"Marchetti, M. A. et al. Prospective validation of dermoscopy-based open-source artificial intelligence for melanoma diagnosis (PROVE-AI study). Npj Digit. Med. 6, 127 (2023).","journal-title":"Npj Digit. Med."},{"key":"2245_CR18","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1038\/s43856-024-00598-5","volume":"4","author":"L Heinlein","year":"2024","unstructured":"Heinlein, L. et al. Prospective multicenter study using artificial intelligence to improve dermoscopic melanoma diagnosis in patient care. Commun. Med. 4, 177 (2024).","journal-title":"Commun. Med."},{"key":"2245_CR19","doi-asserted-by":"publisher","first-page":"1489","DOI":"10.1111\/jdv.20479","volume":"39","author":"NR Kurtansky","year":"2025","unstructured":"Kurtansky, N. R. et al. Effect of patient-contextual skin images in human- and artificial intelligence-based diagnosis of melanoma: Results from the 2020 SIIM - ISIC melanoma classification challenge. J. Eur. Acad. Dermatol. Venereol. 39, 1489\u20131499 (2025).","journal-title":"J. Eur. Acad. Dermatol. Venereol."},{"key":"2245_CR20","first-page":"18","volume":"17","author":"A Bhanot","year":"2024","unstructured":"Bhanot, A., Bassue, J., Ademola, S., Sallee, B. & Allen, P. Fitzpatrick skin type self reporting versus provider reporting: a single-center, survey-based study. J. Clin. Aesthetic Dermatol. 17, 18\u201322 (2024).","journal-title":"J. Clin. Aesthetic Dermatol."},{"key":"2245_CR21","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1001\/jamadermatol.2013.6101","volume":"149","author":"S Eilers","year":"2013","unstructured":"Eilers, S. et al. Accuracy of self-report in assessing Fitzpatrick skin phototypes I through VI. JAMA Dermatol 149, 1289\u20131294 (2013).","journal-title":"JAMA Dermatol"},{"key":"2245_CR22","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1016\/j.jaad.2014.05.023","volume":"71","author":"SY He","year":"2014","unstructured":"He, S. Y. et al. Self-reported pigmentary phenotypes and race are significant but incomplete predictors of Fitzpatrick skin phototype in an ethnically diverse population. J. Am. Acad. Dermatol. 71, 731\u2013737 (2014).","journal-title":"J. Am. Acad. Dermatol."},{"key":"2245_CR23","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1016\/j.jaad.2024.01.067","volume":"91","author":"VM Harvey","year":"2024","unstructured":"Harvey, V. M. et al. Integrating skin color assessments into clinical practice and research: a review of current approaches. J. Am. Acad. Dermatol. 91, 1189\u20131198 (2024).","journal-title":"J. Am. Acad. Dermatol."},{"key":"2245_CR24","doi-asserted-by":"publisher","unstructured":"Pangelinan, G. et al. The CHROMA-FIT dataset: characterizing human ranges of melanin for increased tone-awareness. In 2024 IEEE\/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) 1170\u20131178 (IEEE, 2024). https:\/\/doi.org\/10.1109\/WACVW60836.2024.00127.","DOI":"10.1109\/WACVW60836.2024.00127"},{"key":"2245_CR25","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-025-04870-8","volume":"12","author":"N Fong","year":"2025","unstructured":"Fong, N. et al. Open access dataset and common data model for pulse oximeter performance data. Sci. Data 12, 570 (2025).","journal-title":"Sci. Data"},{"key":"2245_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2024.105051","volume":"102","author":"G Leeb","year":"2024","unstructured":"Leeb, G. et al. The performance of 11 fingertip pulse oximeters during hypoxemia in healthy human participants with varied, quantified skin pigment. EBioMedicine 102, 105051 (2024).","journal-title":"EBioMedicine"},{"key":"2245_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3632120","volume":"1","author":"CM Heldreth","year":"2024","unstructured":"Heldreth, C. M. et al. Which skin tone measures are the most inclusive? An investigation of skin tone measures for artificial intelligence. ACM J. Responsible Comput. 1, 1\u201321 (2024).","journal-title":"ACM J. Responsible Comput."},{"key":"2245_CR28","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.jid.2019.11.003","volume":"140","author":"BCK Ly","year":"2020","unstructured":"Ly, B. C. K., Dyer, E. B., Feig, J. L., Chien, A. L. & Del Bino, S. Research techniques made simple: cutaneous colorimetry: a reliable technique for objective skin color measurement. J. Invest. Dermatol. 140, 3\u201312.e1 (2020).","journal-title":"J. Invest. Dermatol."},{"key":"2245_CR29","doi-asserted-by":"publisher","first-page":"e187","DOI":"10.1097\/BCR.0b013e318264bf7d","volume":"34","author":"M van der Wal","year":"2013","unstructured":"van der Wal, M. et al. Objective color measurements: clinimetric performance of three devices on normal skin and scar tissue. J. Burn Care Res. 34, e187\u2013e194 (2013).","journal-title":"J. Burn Care Res."},{"key":"2245_CR30","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1111\/j.1467-2494.1991.tb00561.x","volume":"13","author":"A Chardon","year":"1991","unstructured":"Chardon, A., Cretois, I. & Hourseau, C. Skin colour typology and suntanning pathways. Int. J. Cosmet. Sci. 13, 191\u2013208 (1991).","journal-title":"Int. J. Cosmet. Sci."},{"key":"2245_CR31","first-page":"51","volume":"11","author":"O Inel","year":"2023","unstructured":"Inel, O., Draws, T. & Aroyo, L. Collect, measure, repeat: reliability factors for responsible AI data collection. Proc. AAAI Conf. Hum. Comput. Crowdsourcing 11, 51\u201364 (2023).","journal-title":"Proc. AAAI Conf. Hum. Comput. Crowdsourcing"},{"key":"2245_CR32","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-025-04382-5","volume":"12","author":"K Abhishek","year":"2025","unstructured":"Abhishek, K., Jain, A. & Hamarneh, G. Investigating the quality of dermaMNIST and Fitzpatrick17k dermatological image datasets. Sci. Data 12, 196 (2025).","journal-title":"Sci. Data"},{"key":"2245_CR33","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1111\/bjd.20766","volume":"186","author":"Y Oh","year":"2022","unstructured":"Oh, Y., Markova, A., Noor, S. J. & Rotemberg, V. Standardized clinical photography considerations in patients across skin tones. Br. J. Dermatol. 186, 352\u2013354 (2022).","journal-title":"Br. J. Dermatol."},{"key":"2245_CR34","doi-asserted-by":"publisher","unstructured":"Barrett, T., Chen, Q. Z. & Zhang, A. X. Skin deep: investigating subjectivity in skin tone annotations for computer vision benchmark datasets. https:\/\/doi.org\/10.48550\/ARXIV.2305.09072 (2023).","DOI":"10.48550\/ARXIV.2305.09072"},{"key":"2245_CR35","doi-asserted-by":"publisher","unstructured":"Krishnapriya, K., King, M. C. & Bowyer, K. W. Analysis of Manual and automated skin tone assignments for face recognition applications. Preprint at https:\/\/doi.org\/10.48550\/ARXIV.2104.14685 (2021).","DOI":"10.48550\/ARXIV.2104.14685"},{"key":"2245_CR36","doi-asserted-by":"publisher","first-page":"e2446615","DOI":"10.1001\/jamanetworkopen.2024.46615","volume":"7","author":"A Ward","year":"2024","unstructured":"Ward, A. et al. Creating an empirical dermatology dataset through crowdsourcing with web search advertisements. JAMA Netw. Open 7, e2446615 (2024).","journal-title":"JAMA Netw. Open"},{"key":"2245_CR37","doi-asserted-by":"publisher","first-page":"23771","DOI":"10.1007\/s11042-022-14211-1","volume":"82","author":"A Corbin","year":"2023","unstructured":"Corbin, A. & Marques, O. Exploring strategies to generate Fitzpatrick skin type metadata for dermoscopic images using individual typology angle techniques. Multimed. Tools Appl. 82, 23771\u201323795 (2023).","journal-title":"Multimed. Tools Appl."},{"key":"2245_CR38","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1007\/s10462-016-9491-9","volume":"46","author":"J Zhang","year":"2016","unstructured":"Zhang, J., Wu, X. & Sheng, V. S. Learning from crowdsourced labeled data: a survey. Artif. Intell. Rev. 46, 543\u2013576 (2016).","journal-title":"Artif. Intell. Rev."},{"key":"2245_CR39","doi-asserted-by":"publisher","DOI":"10.2196\/51397","volume":"26","author":"NM Duggan","year":"2024","unstructured":"Duggan, N. M. et al. Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Data Set Labeling: Prospective Analysis. J. Med. Internet Res. 26, e51397 (2024).","journal-title":"J. Med. Internet Res."},{"key":"2245_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3555634","volume":"6","author":"M Groh","year":"2022","unstructured":"Groh, M., Harris, C., Daneshjou, R., Badri, O. & Koochek, A. Towards transparency in dermatology image datasets with skin tone annotations by experts, crowds, and an algorithm. Proc. ACM-Hum.-Comput. Interact. 6, 1\u201326 (2022).","journal-title":"Proc. ACM-Hum.-Comput. Interact."},{"key":"2245_CR41","doi-asserted-by":"publisher","first-page":"108044","DOI":"10.1016\/j.cmpb.2024.108044","volume":"245","author":"M Ben\u010devi\u0107","year":"2024","unstructured":"Ben\u010devi\u0107, M., Habijan, M., Gali\u0107, I., Babin, D. & Pi\u017eurica, A. Understanding skin color bias in deep learning-based skin lesion segmentation. Comput. Methods Prog. Biomed. 245, 108044 (2024).","journal-title":"Comput. Methods Prog. Biomed."},{"key":"2245_CR42","doi-asserted-by":"publisher","first-page":"1313","DOI":"10.1093\/sf\/sou007","volume":"92","author":"EP Monk","year":"2014","unstructured":"Monk, E. P. Skin tone stratification among Black Americans, 2001-2003. Soc. Forces 92, 1313\u20131337 (2014).","journal-title":"Soc. Forces"},{"key":"2245_CR43","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1037\/0096-3445.135.4.501","volume":"135","author":"DT Levin","year":"2006","unstructured":"Levin, D. T. & Banaji, M. R. Distortions in the perceived lightness of faces: the role of race categories. J. Exp. Psychol. Gen. 135, 501\u2013512 (2006).","journal-title":"J. Exp. Psychol. Gen."},{"key":"2245_CR44","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1146\/annurev-soc-060116-053315","volume":"43","author":"AR Dixon","year":"2017","unstructured":"Dixon, A. R. & Telles, E. E. Skin color and colorism: global research, concepts, and measurement. Annu. Rev. Sociol. 43, 405\u2013424 (2017).","journal-title":"Annu. Rev. Sociol."},{"key":"2245_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3730409","volume":"2","author":"C Cook","year":"2025","unstructured":"Cook, C. et al. Colorimetric skin tone scale for improved accuracy of human skin tone annotations. ACM J. Responsible Comput. 2, 1\u201323 (2025).","journal-title":"ACM J. Responsible Comput."},{"key":"2245_CR46","doi-asserted-by":"publisher","unstructured":"Groh, M. et al. Evaluating deep neural networks trained on clinical images in dermatology with the Fitzpatrick 17k dataset, https:\/\/doi.org\/10.48550\/ARXIV.2104.09957 (2021).","DOI":"10.48550\/ARXIV.2104.09957"},{"key":"2245_CR47","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw, P. J. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53\u201365 (1987).","journal-title":"J. Comput. Appl. Math."},{"key":"2245_CR48","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1109\/TPAMI.1979.4766909","volume":"1","author":"DL Davies","year":"1979","unstructured":"Davies, D. L. & Bouldin, D. W. A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1, 224\u2013227 (1979).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02245-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02245-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02245-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T20:33:08Z","timestamp":1767040388000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02245-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,22]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2245"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-02245-2","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,22]]},"assertion":[{"value":"22 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"V.R. has received funding from the National Institutes of Health, the Melanoma Research Alliance, the Department of Defense Congressionally Directed Medical Research Programs, the Prevent Cancer Foundation, and the Burroughs Wellcome Fund. V.R. has received research support (in kind) from Kaggle and Amazon Web Services and is a site investigator for Lutris Pharma. V.R. consults for Inhabit Brands Inc and Atria Institute. V.R. serves on the American Academy of Dermatology Augmented Intelligence Committee, Society for Imaging Informatics in Medicine Board, and International Skin Imaging Collaboration AI Working Group. The other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"787"}}