{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T06:15:31Z","timestamp":1783318531080,"version":"3.54.6"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T00:00:00Z","timestamp":1783296000000},"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>Artificial intelligence is often used in studies that detect skin cancer, but most work uses images of selected lesions. These images are useful for classification. However, they do not fully match real screening situations. In lesion surveillance, clinicians need to inspect many lesions across the whole body and monitor whether lesions change over time. Three-dimensional total-body photography is able to capture a wider skin surface and can support lesion selection, lesion triage, risk assessment, and follow-up comparison. This approach gives artificial intelligence more opportunity to analyze the patient beyond one selected lesion. This Mini Review discusses recent evidence on artificial intelligence-assisted three-dimensional total-body photography for skin cancer surveillance. In current applications, this approach is also used in automated triage and lesion detection, multimodal risk prediction, phenotype extraction, and longitudinal tracking. The reviewed papers show early progress, but the evidence remains limited. Skin tone reporting, workflow integration, dataset transparency, false-positive and false-negative harm, cost, and equity remain key adoption issues. The objective is to focus on the shift from selected-lesion classification to whole-body imaging. Clinical value should not be judged only by lesion-level accuracy, but also by whether artificial intelligence-assisted three-dimensional total-body photography can improve patient-level surveillance across the whole skin surface and over time. Stronger prospective, longitudinal, workflow, cost, and equity evidence is still needed before routine clinical adoption in practice.<\/jats:p>","DOI":"10.3389\/fmed.2026.1882075","type":"journal-article","created":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T05:38:59Z","timestamp":1783316339000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence in three-dimensional total-body photography for skin cancer surveillance"],"prefix":"10.3389","volume":"13","author":[{"given":"Kai Liang","family":"Lew","sequence":"first","affiliation":[{"name":"Faculty of Engineering and Technology, Multimedia University","place":["Melaka, Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kok Swee","family":"Sim","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Technology, Multimedia University","place":["Melaka, Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wai Ti","family":"Chan","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Technology, Multimedia University","place":["Melaka, Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,6]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"3896","DOI":"10.3390\/cancers17243896","article-title":"Artificial intelligence and new technologies in melanoma diagnosis: a narrative review","volume":"17","author":"G\u00f3recki","year":"2025","journal-title":"Cancers"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1016\/j.jid.2023.11.007","article-title":"A narrative review: opportunities and challenges in artificial intelligence skin image analyses using total body photography","volume":"144","author":"Primiero","year":"2024","journal-title":"J Invest Dermatol"},{"key":"B3","doi-asserted-by":"publisher","first-page":"e68510","DOI":"10.2196\/68510","article-title":"3D total body photography as a promising innovation for early skin cancer detection: scoping review","volume":"8","author":"Baete","year":"2025","journal-title":"JMIR Dermatol"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1016\/j.ad.2024.09.030","article-title":"Melanoma diagnosis with 3D total-body photography","volume":"116","author":"Ferreirinha","year":"2025","journal-title":"Actas Dermo-Sifiliogr\u00e1ficas"},{"key":"B5","doi-asserted-by":"publisher","first-page":"12697","DOI":"10.1038\/s41598-024-61681-4","article-title":"Automatized self-supervised learning for skin lesion screening","volume":"14","author":"Useini","year":"2024","journal-title":"Sci Rep"},{"key":"B6","doi-asserted-by":"publisher","first-page":"884","DOI":"10.1038\/s41597-024-03743-w","article-title":"The SLICE-3D dataset: 400,000 skin lesion image crops extracted from 3D TBP for skin cancer detection","volume":"11","author":"Kurtansky","year":"2024","journal-title":"Sci Data"},{"key":"B7","doi-asserted-by":"publisher","first-page":"AIdbp2400732","DOI":"10.1056\/AIdbp2400732","article-title":"Multimodal image dataset for AI-based skin cancer (MIDAS) benchmarking","volume":"2","author":"Chiou","year":"2025","journal-title":"NEJM AI"},{"key":"B8","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1038\/s41746-025-02070-7","article-title":"Automated triage of cancer-suspicious skin lesions with 3D total-body photography","volume":"8","author":"Kurtansky","year":"2025","journal-title":"NPJ Dig Med"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1442","DOI":"10.1038\/s41597-025-05483-x","article-title":"Skin region images extracted from 3D total body photographs for lesion detection","volume":"12","author":"Saha","year":"2025","journal-title":"Sci Data"},{"key":"B10","doi-asserted-by":"publisher","first-page":"45139","DOI":"10.1038\/s41598-025-33536-z","article-title":"Explainable multimodal AI for skin lesion risk prediction via 3D imaging and clinical data","volume":"15","author":"Wang","year":"2025","journal-title":"Sci Rep"},{"key":"B11","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1093\/bjd\/ljaf516","article-title":"Automated classification of site-specific cutaneous photodamage using a convolutional neural network and three-dimensional total body photography","volume":"194","author":"Kahler","year":"2025","journal-title":"Br J Dermatol"},{"key":"B12","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1093\/bjd\/ljaf440","article-title":"Three-dimensional total body photography identifies cutaneous phenotypes associated with late-onset invasive melanoma risk","volume":"194","author":"Kahler","year":"2025","journal-title":"Br J Dermatol"},{"key":"B13","doi-asserted-by":"publisher","first-page":"e70224","DOI":"10.1002\/ima.70224","article-title":"Optimizing skin cancer classification with ResNet-18: a scalable approach with 3D total body photography (3D-TBP)","volume":"35","author":"Rashid","year":"2025","journal-title":"Int J Imag Syst Technol"},{"key":"B14","doi-asserted-by":"publisher","first-page":"2691","DOI":"10.1038\/s41591-025-03747-y","article-title":"A multimodal vision foundation model for clinical dermatology","volume":"31","author":"Yan","year":"2025","journal-title":"Nat Med"},{"key":"B15","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1159\/000541894","article-title":"Three-dimensional total body photography, digital dermoscopy, and in vivo reflectance confocal microscopy for follow-up assessments of high-risk patients for melanoma: a prospective, controlled study","volume":"240","author":"Hobelsberger","year":"2024","journal-title":"Dermatology"},{"key":"B16","doi-asserted-by":"publisher","first-page":"103963","DOI":"10.1016\/j.media.2026.103963","article-title":"Revisiting lesion tracking in 3D total body photography","volume":"110","author":"Huang","year":"2026","journal-title":"Med Image Anal"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1380984","DOI":"10.3389\/fmed.2024.1380984","article-title":"A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification","volume":"11","author":"Primiero","year":"2024","journal-title":"Front Med"},{"key":"B18","doi-asserted-by":"publisher","first-page":"1602","DOI":"10.1038\/s41597-025-05880-2","article-title":"A longitudinal dataset of tile and corresponding dermoscopic images with metadata for identifying skin cancers","volume":"12","author":"Ghahari","year":"2025","journal-title":"Sci Data"},{"key":"B19","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1038\/s41746-025-02125-9","article-title":"Improving dataset transparency in dermatologic artificial intelligence using a dataset nutrition label","volume":"8","author":"Li","year":"2025","journal-title":"NPJ Dig Med"},{"key":"B20","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1038\/s41746-025-02245-2","article-title":"Evaluating skin tone scales for dermatologic dataset labeling: a prospective-comparative study","volume":"8","author":"Weir VR Li","year":"2025","journal-title":"NPJ Dig Med"},{"key":"B21","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1038\/s41746-025-01770-4","article-title":"Beyond fitzpatrick: automated artificial intelligence-based skin tone analysis in dermatological patients","volume":"8","author":"Ulrich","year":"2025","journal-title":"NPJ Dig Med"},{"key":"B22","doi-asserted-by":"publisher","first-page":"5353","DOI":"10.5826\/dpc.1502a5353","article-title":"Artificial intelligence-based image analysis is insufficient as a stand-alone assessment of skin tumors in real clinical practice","volume":"15","author":"Lallas","year":"2025","journal-title":"Dermatol Pract Concept"},{"key":"B23","doi-asserted-by":"publisher","first-page":"4896","DOI":"10.5826\/dpc.1502a4896","article-title":"International survey on dermoscopic image management: ISIC data on capture, storage, and AI integration in dermatology","volume":"15","author":"Parvathaneni","year":"2025","journal-title":"Dermatol Pract Concept"},{"key":"B24","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.1016\/j.jaad.2025.06.003","article-title":"Remote evaluation of general skin diseases using three-dimensional total body photography: an observer agreement study","volume":"93","author":"Sitaru","year":"2025","journal-title":"J Am Acad Dermatol"},{"key":"B25","doi-asserted-by":"publisher","first-page":"100038","DOI":"10.1016\/j.ejcskn.2024.100038","article-title":"Comparison of the efficacy of skin examination using 3D total body photography to clinical and dermoscopic examination","volume":"2","author":"Gellrich","year":"2024","journal-title":"EJC Skin Cancer"},{"key":"B26","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1159\/000535031","article-title":"Clinician's ability to identify non-melanoma skin cancer on 3D-total body photography sectors that were initially identified during in-person skin examination with dermoscopy","volume":"240","author":"Hobelsberger","year":"2024","journal-title":"Dermatology"},{"key":"B27","doi-asserted-by":"publisher","first-page":"2186","DOI":"10.3390\/medicina61122186","article-title":"Equity and generalizability of artificial intelligence for skin-lesion diagnosis using clinical, dermoscopic, and smartphone images: a systematic review and meta-analysis","volume":"61","author":"Tjiu","year":"2025","journal-title":"Medicina"},{"key":"B28","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1553\/skindeep.2025.150261","article-title":"The deep imaging phenotype for melanoma risk stratification","volume":"1","author":"Kahler","year":"2025","journal-title":"SKINdeep"},{"key":"B29","doi-asserted-by":"publisher","first-page":"1331895","DOI":"10.3389\/fmed.2024.1331895","article-title":"Artificial intelligence and skin cancer","volume":"11","author":"Wei","year":"2024","journal-title":"Front Med"},{"key":"B30","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1038\/s41746-024-01176-8","article-title":"A survey of skin tone assessment in prospective research","volume":"7","author":"Weir","year":"2024","journal-title":"NPJ Dig Med"}],"container-title":["Frontiers in Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1882075\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T05:38:59Z","timestamp":1783316339000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1882075\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,6]]},"references-count":30,"alternative-id":["10.3389\/fmed.2026.1882075"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2026.1882075","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,6]]},"article-number":"1882075"}}