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Applications of AI (366) Ethics, Privacy, and Legal Issues (676) Demographics of Users, Social & Digital Divide (983) Equity and Digital Divide (430) Artificial Intelligence (2416) Systemic Racism and Racial Bias in Health Care and Society (138) Ethical, Legal, and Social Issues in AI (131)

Published on in Vol 3 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58275, first published .
Ensuring Appropriate Representation in Artificial Intelligence–Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias

Ensuring Appropriate Representation in Artificial Intelligence–Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias

Ensuring Appropriate Representation in Artificial Intelligence–Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias

Authors of this article:

Andrew O'Malley1 Author Orcid Image ;   Miriam Veenhuizen1 Author Orcid Image ;   Ayla Ahmed1 Author Orcid Image
Article Authors Cited by (12) Tweetations (5) Metrics Author Video

Journals

  1. Ahmed A, Kerr E, O’Malley A. Quality assurance and validity of AI-generated single best answer questions. BMC Medical Education 2025;25(1) View
  2. Haider S, Prabha S, Gomez-Cabello C, Borna S, Pressman S, Genovese A, Trabilsy M, Galvao A, Aziz K, Murray P, Parte Y, Yu Y, Tao C, Forte A. A Validity Analysis of Text-to-Image Generative Artificial Intelligence Models for Craniofacial Anatomy Illustration. Journal of Clinical Medicine 2025;14(7):2136 View
  3. Lederman O, Llana A, Murray J, Stanton R, Chugh R, Haywood D, Burdett A, Warman G, Walker J, Hart N. Promises and perils of generative artificial intelligence: a narrative review informing its ethical and practical applications in clinical exercise physiology. BMC Sports Science, Medicine and Rehabilitation 2025;17(1) View
  4. M'gadzah S, O'Malley A. Enhancing Diagnostic Accuracy of Ophthalmological Conditions With Complex Prompts in GPT-4: Comparative Analysis of Global and Low- and Middle-Income Country (LMIC)–Specific Pathologies. JMIR Formative Research 2025;9:e64986 View
  5. Mainardi V, Canto M, Melillo T, Lorenzini N, Bagnoni G, Moccia S, Ciuti G. A Thermal-Imaging System and Machine-Learning Classification Algorithm for Skin Cancer Screening. IEEE Transactions on Medical Robotics and Bionics 2025;7(3):938 View
  6. Kumar R, Kim Y, Byun Y. Hybrid Framework Combining Diffusion-Based Image Augmentation and Feature Level SMOTE for Addressing Extreme Class Imbalance. IEEE Access 2025;13:154623 View
  7. Zhao Y, Chen B, Shen L, Xi J. AI‐generated dermatologic images: A call for clinical accuracy and inclusive design. Journal of the European Academy of Dermatology and Venereology 2025 View
  8. Veenhuizen M, O’Malley A. Demographic biases in AI-generated simulated patient cohorts: a comparative analysis against census benchmarks. Advances in Simulation 2025;10(1) View
  9. Lim P. Responsible AI in medical aesthetics: a critical review of facial and skin analysis applications and ethical considerations. AI and Ethics 2026;6(1) View
  10. Altaf A, Altaf A, Rehman F. Modified EfficientNet-B0 Architecture Optimized with Quantum-Behaved Algorithm for Skin Cancer Lesion Assessment. Diagnostics 2025;15(24):3245 View
  11. Alon L, Shoval D, Levkovich I. Bias, representation, and clinical fidelity in AI-generated images for medical education: a systematic literature review. npj Digital Medicine 2026 View

Conference Proceedings

  1. Liu X, Jiang X. Proceedings of the 2nd International Conference on Intelligent Education and Computer Technology. Artificial Intelligence Generated Content Enabling Education Meta-Universe: Development Status and Path Optimization View