Comprehensive Summary
This article examines the ethical limitations of AI in dermatology by responding to concerns raised in the letter "Hidden Patterns and Overlooked Pitfalls in AI-Generated Dermatology Images: Beyond Surface Diversity." The authors highlight significant bias in foundational training datasets, noting that 89.8% of over 4,000 AI-generated dermatology images depict light skin tones. Among the four generative AI platforms analyzed, only Adobe Firefly demonstrated inclusion of diverse skin tones. Furthermore, 56% of 80 condition-specific image searches yielded no representations of individuals with Fitzpatrick skin types V–VI, supporting the perception of ‘tokenistic’ diversity. To address these flaws, the article advocates for stratifying AI performance by skin tone to ensure more equitable representation. It also calls for a shift away from U.S. Census–based ethnicity classifications toward more precise phenotypic measures and recommends the systematic evaluation of prompt variations to mitigate bias in image generation.
Outcomes and Implications
Limited and biased datasets hinder the development of equitable AI performance in dermatology. Selective training of AI programs lead to “diagnostic invisibility,” emphasizing the need for improved phenotypic measure and to identify gaps in clinical applicability. Although existing biases complicate interpretation of AI outputs, ongoing research – such as reviews of clinical AI, the Diverse Dermatology Images study, and seminal work in population-health algorithms – all call attention to improvement in AI performance. To move toward fairness, the article outlines four practical steps. First, openly accessible image banks addressing the full Fitzpatrick spectrum should be developed. Second, stratified performance should be mandatory. Third, systematic prompt-engineering audits are necessary. Fourth, equitable surveillance should be built as a standard feature. Together, these measures would ultimately lead to advance equitable skin-health care with representative data and transparent evaluation for all skin types.