According to the researchers, the shift often happened among treatment options that were all clinically appropriate, which can make the influence hard to spot.
“What I find quite interesting, and even surprising, was how reasonable the answers often appeared,” said first author Mahmud Omar, MD, a physician-scientist and lecturer in the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine. “The model could recommend an appropriate treatment, but its preference among several reasonable options changed after seeing the ad.”
Omar, who leads research on the safety, reliability and real-world effects of generative AI in clinical care, said that because the final answer could still look medically sound, “that influence could be easy to miss in typical research evaluations” and in personal use by patients and physicians.
The models also rarely acknowledged the ad. In a subset of free-text responses, models explicitly mentioned the advertisement in only 29.9% of responses, though they repeated information or claims from the ad more often, the researchers reported.
“An answer can be clinically reasonable and still raise an important question about why the AI arrived at that answer,” said senior author Girish N. Nadkarni, MD, MPH, chair of the Windreich Department and chief AI officer of the Mount Sinai Health System. “As AI becomes part of how people get health information, we need to be able to distinguish clinical guidance from commercial influence and be transparent about when advertising is part of the interaction.”
The investigators cautioned that the results do not mean drug advertising will necessarily lead AI models to recommend unsafe or inappropriate treatments. In the study, ads did not clearly increase recommendations for unsupported supplements or clearly suboptimal drugs. The researchers also did not examine whether changes in AI recommendations affected prescriptions, patient decisions, costs or health outcomes.
The researchers said the findings point to several practical steps for AI developers and health systems: testing AI systems with and without promotional content to see whether recommendations change, clearly disclosing when advertising is presented to an AI system and keeping promotional material separate from clinical advice.
The study may also matter to regulators as they weigh how
The Mount Sinai team plans to extend the work through its ongoing evaluation of AI and human-AI interaction in real-world clinical cases.
The paper, “Ad-verse Effects: Pharmaceutical Advertising and Drug Recommendations by Consumer-Facing AI in a Controlled Study,” was supported in part by grants from the National Center for Advancing Translational Sciences and the National Institutes of Health.
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