Large language models propagate race-based medicine.

Large language models (LLMs) are being integrated into healthcare systems; but these models may recapitulate harmful, race-based medicine. The objective of this study is to assess whether four commercially available large language models (LLMs) propagate harmful, inaccurate, race-based content when responding to eight different scenarios that check for race-based medicine or widespread misconceptions around race. Questions were derived from discussions among four physician experts and prior work on race-based medical misconceptions believed by medical trainees. We assessed four large language models with nine different questions that were interrogated five times each with a total of 45 responses per model. All models had examples of perpetuating race-based medicine in their responses. Models were not always consistent in their responses when asked the same question repeatedly. LLMs are being proposed for use in the healthcare setting, with some models already connecting to electronic health record systems. However, this study shows that based on our findings, these LLMs could potentially cause harm by perpetuating debunked, racist ideas.

Skin Tone Analysis for Representation in Educational Materials (STAR-ED) using machine learning.

Images depicting dark skin tones are significantly underrepresented in the educational materials used to teach primary care physicians and dermatologists to recognize skin diseases. This could contribute to disparities in skin disease diagnosis across different racial groups. Previously, domain experts have manually assessed textbooks to estimate the diversity in skin images. Manual assessment does not scale to many educational materials and introduces human errors. To automate this process, we present the Skin Tone Analysis for Representation in EDucational materials (STAR-ED) framework, which assesses skin tone representation in medical education materials using machine learning. Given a document (e.g., a textbook in .pdf), STAR-ED applies content parsing to extract text, images, and table entities in a structured format. Next, it identifies images containing skin, segments the skin-containing portions of those images, and estimates the skin tone using machine learning. STAR-ED was developed using the Fitzpatrick17k dataset. We then externally tested STAR-ED on four commonly used medical textbooks. Results show strong performance in detecting skin images (0.96 ± 0.02 AUROC and 0.90 ± 0.06 F1 score) and classifying skin tones (0.87 ± 0.01 AUROC and 0.91 ± 0.00 F1 score). STAR-ED quantifies the imbalanced representation of skin tones in four medical textbooks: brown and black skin tones (Fitzpatrick V-VI) images constitute only 10.5% of all skin images. We envision this technology as a tool for medical educators, publishers, and practitioners to assess skin tone diversity in their educational materials.

Skin Cancer in People of Color: A Systematic Review.

BACKGROUND: People of African, Asian, Hispanic or Latino, Pacific Islander, and Native Indian descent are considered people of color by the Skin of Color Society (SOCS). OBJECTIVES: In this study, we assess incidence, risk factors, clinical characteristics, histopathology, treatment, and survival for skin malignancies in people of color as defined by the SOCS, by systematically reviewing the literature. METHODS: An electronic literature search of the PubMed, EMBASE, and MEDLINE databases was performed. Articles published from 1 January 1990 through 12 December 2020 were included in the search. RESULTS: We identified 2666 publications potentially meeting the study criteria. Titles and abstracts of these studies were reviewed and 2353 were excluded. The full text of 313 articles were evaluated and 251 were included in this review. CONCLUSION: Differences in incidence, patterns, treatment, and survival exist among people of color for cutaneous malignancies. Further research and initiatives are needed to account for and mitigate these differences.

Establishing the diagnosis of rosacea in skin of color patients.

Rosacea is a chronic inflammatory cutaneous disorder that may be underreported and underrecognized in skin of color (SOC) patients. There are several skin disorders that can present with the classic features of rosacea, such as erythema, papules, and pustules, which can confound the diagnosis. To promote accurate and timely diagnosis of rosacea, we review possible rosacea mimickers in SOC patients.