Artificial Intelligence in Action: Racial and Gender Disparities in Academic Radiology

TL;DR

This study explores how generative AI can help identify gender and racial disparities in academic radiology, comparing AI outputs to traditional methods.

Contribution

The study evaluates the interpretative capacity of generative AI in analyzing academic radiology disparities, comparing it to human-led analyses.

Findings

Perplexity and DeepSeek provided more detailed insights into disparities compared to ChatGPT.

AI models showed inconsistencies in temporal trends and policy recommendations.

AI can enhance understanding of inequities when used alongside traditional methods and critically evaluated.

Abstract

Academic radiology continues to face persistent gender and racial disparities in career advancement. The emergence of generative artificial intelligence (AI) platforms offers new opportunities to analyze workforce diversity patterns rapidly and at scale. This study aimed to evaluate the interpretative capacity of three generative AI platforms (i.e., ChatGPT, DeepSeek, and Perplexity) in identifying disparities in academic rank and tenure status across gender and racial subgroups in academic radiology. The outputs of these AI models were compared with conventional human-led analyses for accuracy, limitations, and potential biases.

We prompted each AI model to analyze publicly available American Association of Medical Colleges Faculty Roster data on tenure and rank distribution by gender and race using standardized query templates. Outputs were systematically evaluated for consistency, accuracy, and potential biases against benchmark human-curated studies. Comparative analysis included variations between AI platforms and traditional research methods, with particular attention to how each model interpreted and reported disparities.

The AI models broadly recognized trends in faculty growth and underrepresentation, but interpretations varied. Perplexity and DeepSeek provided more granular insights, such as declining tenure rates and intersectional disparities, while ChatGPT offered less detailed analyses. Key discrepancies included divergent temporal trends and policy recommendations, highlighting AI’s limitations in capturing nuanced sociodemographic patterns.

Generative AI shows promise in analyzing workforce disparities but requires validation to mitigate biases and inconsistencies. When used alongside traditional methods, AI can enhance understanding of inequities in academic medicine, provided that its outputs are critically evaluated for fairness and accuracy.

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