Deep learning-aided decision support for diagnosis of skin disease across skin tones
Open accessAbstract
Although advances in deep learning systems for image-based medical diagnosis demonstrate their potential to augment clinical decision-making, the effectiveness of physician–machine partnerships remains an open question, in part because physicians and algorithms are both susceptible to systematic errors, especially for diagnosis of underrepresented populations. Here we present results from a large-scale digital experiment involving board-certified dermatologists ( n = 389) and primary-care physicians ( n = 459) from 39 countries to evaluate the accuracy of diagnoses submitted by physicians in a store-and-forward teledermatology simulation. In this experiment, physicians were presented with 364 images spanning 46 skin diseases and asked to submit up to four differential diagnoses. Specialists and generalists achieved diagnostic accuracies of 38% and 19%, respectively, but both specialists and generalists were four percentage points less accurate for the diagnosis of images of dark skin as compared to light skin. Fair deep learning system decision support improved the diagnostic accuracy of both specialists and generalists by more than 33%, but exacerbated the gap in the diagnostic accuracy of generalists across skin tones. These results demonstrate that well-designed physician–machine partnerships can enhance the diagnostic accuracy of physicians, illustrating that success in improving overall diagnostic accuracy does not necessarily address bias.
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Efforts to leverage generative AI to simulate various skin tones risk exacerbating health inequities and magnifying institutional racism.
“Although some efforts aim to leverage generative AI to simulate various skin tones (Munia and Al Zubaer Imran, 20256), these approaches also risk exacerbating health inequities and magnifying institutional racism (Groh et al, 2024; Grzybowski et al, 2024; Mikołajczyk et al, 20227).”
From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Groh et al, 2024
The Discussion (page unavailable) reports that deep-learning decision support exacerbated diagnostic-accuracy disparities between light and dark skin for non-specialists, supporting the general concern that AI can worsen inequities. But the Main section describes synthetic images made with diffusion models as a potential way to address disparities; the paper does not show that generative simulation of skin tones has this risk or that it magnifies institutional racism. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAI agents possess the capacity to process extensive patient data, including medical histories, diagnostic test results, and imaging, thereby aiding healthcare professionals in making more informed and precise diagnostic decisions.
“As shown in Figure 2A, AI agents possess the capacity to process extensive patient data, including medical histories, diagnostic test results, and imaging, thereby aiding healthcare professionals in making more informed and precise diagnostic decisions.62,63,64,65,66,67,68,69”
From A foundational architecture for AI agents in healthcare (2025) · cites this paper as [65]
The Results (DLS assistance; page unavailable) report that AI-based suggestions improved physicians’ diagnostic accuracy for skin diseases. But the Discussion describes a constrained, single-image task, not AI processing extensive patient data such as medical histories and diagnostic test results. (LLM, conf=0.90)
Experimental — accuracy not yet measuredAlgorithmic bias, particularly related to the underrepresentation of skin of color, continues to limit the generalizability of AI models.
“Algorithmic bias, particularly related to the underrepresentation of skin of color, continues to limit the generalizability of AI models.”
From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Groh et al, 2024
Equitable stand-alone performance may not translate to unbiased human-AI collaboration.
“Another study64 further revealed that equitable stand-alone performance may not translate to unbiased human–AI collaboration, which is crucial for clinical deployment.”
From A Multimodal Vision Foundation Model for Clinical Dermatology (2025) · cites this paper as [64]
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- Authors
- Groh, M., Badri, O., Daneshjou, R., Koochek, A., Harris, C., Soenksen, L. R., Doraiswamy, P. M., & Picard, R.
- Affiliations
- Northwestern University (US)
- Stanford Medicine (US)
- Banner Health (US)
- MIT Media Lab (US)
- Harvard University (US)
- Duke University (US)
- Corresponding author
- Groh, Matthew
- ORCID
- Venue
- Nature Medicine
- Published
- 2024-02
- Volume
- 30
- Issue
- 2
- Pages
- 573-583
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC10878981
- OpenAlex
- W4391528827
- Cited by
- 160
- References
- 80
- Citations by year
- 2026: 67 · 2025: 67 · 2024: 26
- FWCI
- 14.96
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- 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 › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- dermatological diagnosis, skin tone, dark skin, algorithmic bias, teledermatology, differential diagnosis, diagnostic accuracy, dermatologists, primary care physicians, underrepresented groups
- MeSH terms
- Deep Learning, Algorithms, Diagnosis, Differential, Humans, Skin Diseases, Skin Pigmentation
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