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Papers of the Week


Papers: 14 Sep 2024 - 20 Sep 2024


2024 Sep 06


Pain


39283333

Racial, ethnic, and sex bias in large language model opioid recommendations for pain management.

Authors

Young CC, Enichen E, Rao A, Succi MD

Abstract

Understanding how large language model (LLM) recommendations vary with patient race/ethnicity provides insight into how LLMs may counter or compound bias in opioid prescription. Forty real-world patient cases were sourced from the MIMIC-IV Note dataset with chief complaints of abdominal pain, back pain, headache, or musculoskeletal pain and amended to include all combinations of race/ethnicity and sex. Large language models were instructed to provide a subjective pain rating and comprehensive pain management recommendation. Univariate analyses were performed to evaluate the association between racial/ethnic group or sex and the specified outcome measures-subjective pain rating, opioid name, order, and dosage recommendations-suggested by 2 LLMs (GPT-4 and Gemini). Four hundred eighty real-world patient cases were provided to each LLM, and responses included pharmacologic and nonpharmacologic interventions. Tramadol was the most recommended weak opioid in 55.4% of cases, while oxycodone was the most frequently recommended strong opioid in 33.2% of cases. Relative to GPT-4, Gemini was more likely to rate a patient’s pain as “severe” (OR: 0.57 95% CI: [0.54, 0.60]; P < 0.001), recommend strong opioids (OR: 2.05 95% CI: [1.59, 2.66]; P < 0.001), and recommend opioids later (OR: 1.41 95% CI: [1.22, 1.62]; P < 0.001). Race/ethnicity and sex did not influence LLM recommendations. This study suggests that LLMs do not preferentially recommend opioid treatment for one group over another. Given that prior research shows race-based disparities in pain perception and treatment by healthcare providers, LLMs may offer physicians a helpful tool to guide their pain management and ensure equitable treatment across patient groups.