LLM-Guided Pain Management: Examining Socio-Demographic Gaps in Cancer vs non-Cancer cases.

ABSTRACT: Large language models (LLMs) offer potential benefits in clinical care. However, concerns remain regarding socio-demographic biases embedded in their outputs. Opioid prescribing is one domain in which these biases can have serious implications, especially given the ongoing opioid epidemic and the need to balance effective pain management with addiction risk. We tested ten LLMs-both open-access and closed-source-on 1,000 acute-pain vignettes. Half of the vignettes were labeled as non-cancer and half as cancer. Each vignette was presented in 34 socio-demographic variations, including a control group without demographic identifiers. We analyzed the models' recommendations on opioids, anxiety treatment, perceived psychological stress, risk scores, and monitoring recommendations. Overall, yield

SUBMITTER: Omar M 

PROVIDER: S-EPMC11908302 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature