Recognizing “Conformity Bias” in Large Language Models: A New Risk for Clinical Use

Objectives The aim of the present study is to systematically investigate the phenomenon of Conformity Bias in contemporary LLMs, specifically evaluating how repeated probing with incorrect information influences model outputs in a clinical context.

4 LLMs including GPT-4o, Gemini-1.5 Flash, Claude-3 Haiku, and GPT-o1 were systematically evaluated through 20 clinical questions focused on ocular disease treatments. Standard queries were followed by probing questions suggesting incorrect treatments. Model responses were analyzed to assess the emergence of Conformity Bias and compared using chi-squared testing.

Correct response rates after successive probing questions were alarmingly low: 25% (GPT-4o), 10% (Gemini-1.5 Flash), 0% (Claude-3 Haiku), and 25% (GPT-o1) (P < 0.001). Across models, the tendency to conform to incorrect user suggestions increased with repeated probing.

Conformity Bias represents a dynamic, user-induced vulnerability in LLMs, distinguishable from training-dependent biases. Its presence underscores the necessity for model designs resistant to misleading user interactions and emphasizes the importance of cross-verification with clinical guidelines. As healthcare systems increasingly integrate AI tools, understanding and mitigating Conformity Bias is imperative to protect patient safety and maintain clinical integrity. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study did not receive any funding Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data availability statement All data produced in the present study are available upon reasonable request to the authors