Disparities in AI‐Based Prior Authorization for Head and Neck Reconstruction: A Large Language Model Analysis
- 1Department of Otolaryngology—Head and Neck Surgery Stanford University School of Medicine Palo Alto California USA
- 2Department of Quantitative and Computational Biology, Dornsife College of Letters, Arts, and Sciences University of Southern California Los Angeles California USA
Abstract
Introduction Large language models (LLM) are being rapidly integrated into healthcare, particularly to streamline time‐ and labor‐intensive administrative processes. However, the potential for artificial intelligence (AI) systems to demonstrate bias when employed for insurance authorization remains poorly understood. As insurers increasingly adopt AI to make coverage decisions, this study examined bias in LLM‐driven prior authorization in otolaryngology, using oral cavity squamous cell carcinoma (SCC) as a case study. Methods Using OpenAI's generative transformer GPT‐4o, this study assessed for LLM bias when simulating insurance coverage decisions for head and neck cancer reconstruction. A standardized clinical scenario was constructed involving patients with T2N2 oral cavity SCC, all requiring surgical resection and reconstruction. The LLM was prompted to choose between a radial forearm free flap (RFFF) and split‐thickness skin graft (STSG) for reconstruction, across 19,900 simulations. Patient profiles were systematically varied by age, sex, race/ethnicity, zip code‐based income level, socioeconomic status (SES), and substance use history. Results The LLM output showed significant disparities in approval decisions. RFFF was more frequently approved for younger, Asian, or white patients from high‐income zip codes or high SES backgrounds (p < 0.0001). Older, Black, and Hispanic patients, and those from lower‐income areas or with substance use histories, were less likely to receive RFFF authorization (p < 0.0001). On sensitivity analysis, inclusion of tumor‐specific information markedly skewed recommendations towards RFFF across sociodemographic backgrounds. Conclusion In this experimental study, the LLM's outputs exhibited significant disparities for oral cavity cancer reconstruction based on patient demographic variables in the setting of limited clinical information. Inputs to LLMs for clinical decision‐making should include pertinent and detailed information to reduce the risk of bias. As insurers increasingly integrate AI for prior authorization, recognition of its biases, rigorous safeguards, and increased regulatory governance are needed to promote equitable health care.
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BibTeX
@article{wu2026disparities,
title = {{Disparities in AI‐Based Prior Authorization for Head and Neck Reconstruction: A Large Language Model Analysis}},
author = {Shannon S. Wu and Mugil V. Shanmugam and Yu‐Jin Lee and Noel F. Ayoub},
journal = {World Journal of Otorhinolaryngology-Head and Neck Surgery},
year = {2026},
month = oct,
doi = {10.1002/wjo2.70130},
url = {https://doi.org/10.1002/wjo2.70130},
note = {CC BY},
}
RIS
TY - JOUR
TI - Disparities in AI‐Based Prior Authorization for Head and Neck Reconstruction: A Large Language Model Analysis
AU - Shannon S. Wu
AU - Mugil V. Shanmugam
AU - Yu‐Jin Lee
AU - Noel F. Ayoub
PY - 2026
DA - 2026/10/23
JO - World Journal of Otorhinolaryngology-Head and Neck Surgery
DO - 10.1002/wjo2.70130
UR - https://doi.org/10.1002/wjo2.70130
AB - Introduction Large language models (LLM) are being rapidly integrated into healthcare, particularly to streamline time‐ and labor‐intensive administrative processes. However, the potential for artificial intelligence (AI) systems to demonstrate bias when employed for insurance authorization remains poorly understood. As insurers increasingly adopt AI to make coverage decisions, this study examined bias in LLM‐driven prior authorization in otolaryngology, using oral cavity squamous cell carcinoma (SCC) as a case study. Methods Using OpenAI's generative transformer GPT‐4o, this study assessed for LLM bias when simulating insurance coverage decisions for head and neck cancer reconstruction. A standardized clinical scenario was constructed involving patients with T2N2 oral cavity SCC, all requiring surgical resection and reconstruction. The LLM was prompted to choose between a radial forearm free flap (RFFF) and split‐thickness skin graft (STSG) for reconstruction, across 19,900 simulations. Patient profiles were systematically varied by age, sex, race/ethnicity, zip code‐based income level, socioeconomic status (SES), and substance use history. Results The LLM output showed significant disparities in approval decisions. RFFF was more frequently approved for younger, Asian, or white patients from high‐income zip codes or high SES backgrounds (p < 0.0001). Older, Black, and Hispanic patients, and those from lower‐income areas or with substance use histories, were less likely to receive RFFF authorization (p < 0.0001). On sensitivity analysis, inclusion of tumor‐specific information markedly skewed recommendations towards RFFF across sociodemographic backgrounds. Conclusion In this experimental study, the LLM's outputs exhibited significant disparities for oral cavity cancer reconstruction based on patient demographic variables in the setting of limited clinical information. Inputs to LLMs for clinical decision‐making should include pertinent and detailed information to reduce the risk of bias. As insurers increasingly integrate AI for prior authorization, recognition of its biases, rigorous safeguards, and increased regulatory governance are needed to promote equitable health care.
SN - 2589-1081
SN - 2095-8811
ER -