Large language models show product name convergence in drug recommendations

Abstract Large language models (LLMs) show promise in supporting drug recommendations both within and outside clinical settings, but they may also amplify systematic biases affecting equity and cost. We examined the commercial neutrality of LLM-generated product name recommendations among US Food and Drug Administration (FDA)-approved products containing the same active ingredient. We evaluated product name recommendations from five LLMs using 177,120 synthetic clinical vignettes spanning 123 disease–drug pairs and 1,440 sociodemographic profiles. Each vignette specified a disease and an active ingredient and asked the model to recommend FDA-approved products. Across disease–drug pairs and models, the most frequently recommended product accounted for a mean of 97.5% of first-choice recommendations, and the models showed almost perfect agreement on which products to recommend. LLM-recommended product distributions differed from real-world prescribing patterns and often yielded higher projected acquisition costs. Recommendations inconsistent with regulatory information varied across LLMs and sociodemographic factors. Models more often recommended products with greater online visibility (Google search counts, odds ratio (OR) 1.70; a dedicated Wikipedia page, OR 3.00) and favoured originator products (OR 20.5), whereas products with later FDA approval and those more commonly prescribed in practice were recommended less often (ORs 0.76 and 0.87, respectively). Although modified prompts shifted recommendations in the intended direction, LLMs still favoured a narrow set of products, which could have potential implications for drug costs and pharmaceutical markets. Rigorous evaluation and targeted mitigation are needed before widespread use.