Current State of Dermatology Mobile Applications With Artificial Intelligence Features
Open accessAbstract
Importance: With advancements in mobile technology and artificial intelligence (AI) methods, there has been a substantial surge in the availability of direct-to-consumer mobile applications (apps) claiming to aid in the assessment and management of diverse skin conditions. Despite widespread patient downloads, these apps exhibit limited evidence supporting their efficacy. Objective: To identify and characterize current English-language AI dermatology mobile apps available for download, focusing on aspects such as purpose, supporting evidence, regulatory status, clinician input, data privacy measures, and use of image data. Evidence Review: In this cross-sectional study, both Apple and Android mobile app stores were systematically searched for dermatology-related apps that use AI algorithms. Each app's purpose, target audience, evidence-based claims, algorithm details, data availability, clinician input during development, and data usage privacy policies were evaluated. Findings: A total of 909 apps were initially identified. Following the removal of 518 duplicates, 391 apps remained. Subsequent review excluded 350 apps due to nonmedical nature, non-English languages, absence of AI features, or unavailability, ultimately leaving 41 apps for detailed analysis. The findings revealed several concerning aspects of the current landscape of AI apps in dermatology. Notably, none of the apps were approved by the US Food and Drug Administration, and only 2 of the apps included disclaimers for the lack of regulatory approval. Overall, the study found that these apps lack supporting evidence, input from clinicians and/or dermatologists, and transparency in algorithm development, data usage, and user privacy. Conclusions and Relevance: This cross-sectional study determined that although AI dermatology mobile apps hold promise for improving access to care and patient outcomes, in their current state, they may pose harm due to potential risks, lack of consistent validation, and misleading user communication. Addressing challenges in efficacy, safety, and transparency through effective regulation, validation, and standardized evaluation criteria is essential to harness the benefits of these apps while minimizing risks.
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Misinformation is rampant, with significant bias, inconsistent validation, and inadequate privacy protection.
“Misinformation is also rampant, with significant bias, inconsistent validation, and inadequate privacy protection (Wongvibulsin et al, 2024).”
From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Wongvibulsin et al, 2024
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Self-citationSmartphone applications for triaging adults with skin lesions that are suspicious for melanoma
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Melatect: A Machine Learning Model Approach For Identifying Malignant Melanoma in Skin GrowthsPreprint
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Artificial intelligence–enabled precision medicine for inflammatory skin diseases
Tang, A. S., Wei, M. L., Haemel, A., La, C., Sirota, M., & Lee, E. Y. (2026) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2025.10.596 · Cited by 6 · see citing works
- Authors
- Wongvibulsin, S., Yan, M. J., Pahalyants, V., Murphy, W., Daneshjou, R., & Rotemberg, V.
- Affiliations
- University of California, Los Angeles (US)
- New York University (US)
- Washington University in St. Louis (US)
- Stanford Medicine (US)
- Memorial Sloan Kettering Cancer Center (US)
- Venue
- JAMA Dermatology
- Publisher
- American Medical Association (AMA)
- Published
- 2024-06-01
- Volume
- 160
- Issue
- 6
- Pages
- 646
- ISSN
- 2168-6068 (ISSN-L)
- PMC
- PMC10921342
- OpenAlex
- W4392543081
- Cited by
- 69
- References
- 12
- Citations by year
- 2026: 29 · 2025: 33 · 2024: 7
- FWCI
- 6.46
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- JAMA Dermatology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion)
- Funders
- National Cancer Institute (P30CA008748)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- mobile health apps, artificial intelligence, skin diseases, regulatory approval, FDA, evidence-based claims, algorithmic transparency, data privacy, image data, app store search
- MeSH terms
- Algorithms, Artificial Intelligence, Cross-Sectional Studies, Dermatology, Humans, Skin Diseases, Mobile Applications
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