A Narrative Review: Opportunities and Challenges in Artificial Intelligence Skin Image Analyses Using Total Body Photography
Open accessAbstract
Artificial intelligence (AI) algorithms for skin lesion classification have reported accuracy at par with and even outperformance of expert dermatologists in experimental settings. However, the majority of algorithms do not represent real-world clinical approach where skin phenotype and clinical background information are considered. We review the current state of AI for skin lesion classification and present opportunities and challenges when applied to total body photography (TBP). AI in TBP analysis presents opportunities for intrapatient assessment of skin phenotype and holistic risk assessment by incorporating patient-level metadata, although challenges exist for protecting patient privacy in algorithm development and improving explainable AI methods.
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Important advances include the application of 3D techniques, such as 3D total body techniques.
“Important advances also include the application of 3D techniques, such as 3D total body techniques [50,51,52].”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [52]
Cites
What is AI? Applications of artificial intelligence to dermatology
Du‐Harpur, X., Watt, F. M., Luscombe, N. M., & Lynch, M. D. (2020) · British Journal of Dermatology · doi:10.1111/bjd.18880 · Cited by 355 · see citing works
Haggenmüller, S., Maron, R. C., Hekler, A., Utikal, J. S., Barata, C., Barnhill, R. L., Beltraminelli, H., Berking, C., Betz-Stablein, B., Blum, A., et al. (2021) · European Journal of Cancer · doi:10.1016/j.ejca.2021.06.049 · Cited by 267 · see citing works
Proposed Technical Guidelines for the Acquisition of Clinical Images of Skin-Related Conditions
Finnane, A., Curiel-Lewandrowski, C., Wimberley, G., Caffery, L., Katragadda, C., Halpern, A., Marghoob, A. A., Malvehy, J., Kittler, H., Hofmann-Wellenhof, R., et al. (2017) · JAMA Dermatology · doi:10.1001/jamadermatol.2016.6214 · Cited by 89 · see citing works
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A Multimodal Vision Foundation Model for Clinical Dermatology
Yan, S., Yu, Z., Primiero, C., Vico-Alonso, C., Wang, Z., Yang, L., Tschandl, P., Hu, M., Ju, L., Tan, G., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03747-y · Cited by 117 · see citing works
“…clinical practice, diagnosing and treating skin conditions involves a range of tasks, including total-body skin cancer detection and risk assessment 2–5 , differential diagnosis of hundreds of dermatological conditions such as inflammatory dermatoses and pigmentary disorders 6 , multimodal image…”
Self-citationDella Mura, M., Sorino, J., Colagrande, A., Daruish, M., Ingravallo, G., Massaro, A., Cazzato, G., Lupo, C., Casatta, N., Ribatti, D., et al. (2025) · Medical Sciences · doi:10.3390/medsci13020070 · Cited by 6 · see citing works
“Important advances also include the application of 3D techniques, such as 3D total body techniques [50–52].”
- Authors
- Primiero, C. A., Rezze, G. G., Caffery, L. J., Carrera, C., Podlipnik, S., Espinosa, N., Puig, S., Janda, M., Soyer, H. P., & Malvehy, J.
- Affiliations
- The University of Queensland (AU)
- Fundació Clínic per a la Recerca Biomèdica (ES)
- Instituto de Salud Carlos III (ES)
- Corresponding author
- Malvehy, Josep
- ORCID
- Venue
- Journal of Investigative Dermatology
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 144
- Issue
- 6
- Pages
- 1200-1207
- ISSN
- 0022-202X (ISSN-L)
- OpenAlex
- W4390956542
- Cited by
- 30
- References
- 3
- Citations by year
- 2026: 10 · 2025: 14 · 2024: 6
- FWCI
- 1.61
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Investigative Dermatology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Health and Medical Research Council (2009923, 2006551, 2020/GNT2007014)
- Horizon 2020
- Australian Cancer Research Foundation (2001517)
- Horizon 2020 Framework Programme (965221)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence, skin lesion classification, total body photography, skin phenotype, patient privacy, explainable AI
- MeSH terms
- Algorithms, Artificial Intelligence, Humans, Image Processing, Computer-Assisted, Photography, Skin, Skin Diseases, Whole Body Imaging
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