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J Neurol Surg B Skull Base
DOI: 10.1055/a-2781-9088
Original Article

Artificial Intelligence-Based Insurance Prior Authorization in Endoscopic Sinus and Skull Base Surgery

Authors

  • Akash S. Halagur

    1   Department of Otolaryngology, Head & Neck Surgery, Stanford University School of Medicine, Stanford, California, United States
    2   Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States

  • Srinidhi Polkampally

    1   Department of Otolaryngology, Head & Neck Surgery, Stanford University School of Medicine, Stanford, California, United States

  • Michael T. Chang

    1   Department of Otolaryngology, Head & Neck Surgery, Stanford University School of Medicine, Stanford, California, United States

  • Noel F. Ayoub

    1   Department of Otolaryngology, Head & Neck Surgery, Stanford University School of Medicine, Stanford, California, United States
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Abstract

Objective

With the rapid adoption of artificial intelligence (AI) in insurance prior authorization (PA) processes, concerns have arisen that these systems may perpetuate or amplify biases. This study investigates demographic biases in AI-based simulations of PA decisions for benign and malignant endoscopic sinonasal and skull base surgeries.

Design

Using a novel Application Programming Interface connected to OpenAI's GPT-4o, PA was simulated for various surgical scenarios: clival chondrosarcoma, pituitary macroadenoma, sinonasal squamous cell carcinoma, and chronic rhinosinusitis.

Setting

Simulated environment within a large language model.

Participants

Four groups of patients with variable characteristics were created based on age, race, and gender (group 1); socioeconomic status (SES) and race (2); and substance use history (3).

Main Outcome Measures

PA specialists were tasked with selecting one patient from groups differing in demographics. All patients within each diagnostic category had identical clinical parameters. Each simulation was repeated 1,000 times per subgroup, with specialists prompted to justify selections and self-assess bias.

Results

Simulated PA decisions demonstrated biases across all surgical scenarios. Young Black patients were most favored, followed by Hispanic patients (all p < 0.001). Younger patients were generally preferred to maximize long-term benefit. For elective sinus surgery, older Black patients were chosen over young Asian patients (p < 0.001). In group 2, Black patients were most preferred, regardless of SES, followed by low-SES Hispanic patients (p < 0.001). In group 3, nonsubstance users were consistently preferred (p < 0.001).

Conclusion

Potential for demographic biases in AI-assisted decision-making must be recognized and mitigated to ensure equitable access to elective and life-saving procedures.

Keywords

artificial intelligence - large language models - prior authorization - bias - endoscopic sinus surgery - skull base surgery - ChatGPT - algorithmic bias

Contributors' Statement

A.S.H.: conceptualization, data curation, formal analysis, methodology, project administration, validation, visualization, and writing—original draft. S.P.: investigation, validation, visualization, and writing—review and editing. M.T.C.: formal analysis, methodology, project administration, validation, visualization, and writing—review and editing. N.F.A.: conceptualization, data curation, formal analysis, investigation, methodology, resources, software, supervision, validation, visualization, and writing—original draft, review, and editing.




Publication History

Received: 17 August 2025

Accepted: 07 January 2026

Article published online:
15 January 2026

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