Evaluating and Addressing Demographic Disparities in Medical Large Language Models: A Systematic Review

Abstract

Background: Large language models (LLMs) are increasingly evaluated for use in healthcare. However, concerns about their impact on disparities persist. This study reviews current research on demographic biases in LLMs to identify prevalent bias types, assess measurement methods, and evaluate mitigation strategies. Methods: We conducted a systematic review, searching publications from January 2018 to July 2024 across five databases. We included peer-reviewed studies evaluating demographic biases in LLMs, focusing on gender, race, ethnicity, age, and other factors. Study quality was assessed using the Joanna Briggs Institute Critical Appraisal Tools. Results: Our review included 24 studies. Of these, 22 (91.7%) identified biases in LLMs. Gender bias was the most prevalent, reported in 15 of 16 studies (93.7%). Racial or ethnic biases were observed in 10 of 11 studies (90.9%). Only two studies found minimal or no bias in certain contexts. Mitigation strategies mainly included prompt engineering, with varying effectiveness. However, these findings are tempered by a potential publication bias, as studies with negative results are less frequently published. Conclusion: Biases are observed in LLMs across various medical domains. While bias detection is improving, effective mitigation strategies are still developing. As LLMs increasingly influence critical decisions, addressing these biases and their resultant disparities is essential for ensuring fair AI systems. Future research should focus on a wider range of demographic factors, intersectional analyses, and non-Western cultural contexts.