Bias Amplification in Intersectional Subpopulations for Clinical Phenotyping by Large Language Models

Large Language Models (LLMs) have demonstrated remarkable performance across diverse clinical tasks. However, there is growing concern that LLMs may amplify human bias and reduce performance quality for vulnerable subpopulations. Therefore, it is critical to investigate algorithmic underdiagnosis in clinical notes, which represent a key source of information for disease diagnosis and treatment. This study examines prevalence of bias in two datasets - smoking and obesity - for clinical phenotyping. Our results demonstrate that state-of-the-art language models selectively and consistently underdiagnosed vulnerable intersectional subpopulations such as young-aged-males for smoking and middle-aged-females for obesity. Deployment of LLMs with such biases risks skewing clinicians’ decision-making which may lead to inequitable access to healthcare. These findings emphasize the need for careful evaluation of LLMs in clinical practice and highlight the potential ethical implications of deploying such systems in disease diagnosis and prognosis. ### 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I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the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 All data produced in the present study are available upon reasonable request to the authors