מילות מפתח: AI, Opiates, Safe AI use, preventive medicine, pain medicine

רקע מדעי ומטרה:

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

שיטות:

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.

תוצאות:

Overall, yielding 3.4 million model-generated responses. we measured how these outputs varied by demographic group and whether a cancer diagnosis intensified or reduced observed disparities. Across both cancer and non-cancer cases, historically marginalized groups—especially cases labeled as individuals who are unhoused, Black, or identify as LGBTQIA+—often received more or stronger opioid recommendations, sometimes exceeding 90% in cancer settings, despite being labeled as high risk by the same models. Meanwhile, low-income or unemployed groups were assigned elevated risk scores yet fewer opioid recommendations, hinting at inconsistent rationales.

מסקנות:

These patterns diverged from standard guidelines and point to model-driven bias rather than acceptable clinical variation.

חשיבות לרפואת המשפחה:

These models, that will inevitably be integrated more in more to preventive and family medicine, show disparities that need the consistent oversight from primary care physicians to insure safe and equitable healthcare