Prior authorization is a well-recognized barrier to timely surgical care, contributing to treatment delays, increased administrative burden, and patients forgoing indicated procedures without demonstrable improvements in clinical outcomes or healthcare expenditures.1 As artificial intelligence (AI) is increasingly deployed within prior authorization workflows, new ethical, clinical, and administrative concerns have emerged that may affect surgeons across specialties.
Recent applications of AI-enabled prior authorization systems have been associated with more than a two-fold increase in denials and error rates approaching 90% in post-acute care cases.2 Additionally, growing evidence suggests that poorly governed AI systems may amplify existing disparities across race, gender, and other demographic factors, raising concerns for equitable access to surgical care.3
Recognizing these shared challenges, the Council of State Neurosurgical Societies (CSNS) established an AI Ad Hoc Committee to evaluate the role of this technology in clinical and academic practice and to inform policy that can be leveraged across organized surgery. The committee developed a comprehensive policy addressing responsible use, privacy and security, transparency, academic integrity, and financial interests in the context of AI adoption in neurosurgery.4
In an effort to generate an empirical foundation for policymaking, the committee also initiated a series of original investigations that addressed the following:
- Evaluation of AI tools in peer review5
- Assessment of journal AI policies6
- Performance analysis of neurosurgery-specific large language models across education and patient communication7-8
- Study of generative models for procedural and anatomical representation9
- Predictive modeling of abstract acceptance and presentation outcomes at national meetings10
Taken together, these efforts position the CSNS methodology as a practical case study in specialty-driven AI governance. Importantly, this policy development framework provides a scalable and reproducible model for other surgical organizations and societies seeking to address AI governance within their own specialties.
Key elements of this approach include establishing a multidisciplinary committee with clinical, academic, and informatics expertise, particularly in the domain of AI. Early alignment with existing ethical and professional standards is essential, alongside generating original empirical data to inform policy decisions.
Intentional engagement with national and state-level advocacy bodies further strengthens the impact and reach of these efforts. By grounding policy in specialty-specific evidence while maintaining alignment with broader surgical and medical principles, societies can ensure that AI guidance remains clinically relevant and evidence-based.
Building on this work, the CSNS passed Resolution XIII-2025F to oppose unmonitored use of AI by commercial insurers in prior authorization processes through engagement with the Washington Committee for Neurological Surgery, which is a joint committee of the Congress of Neurological Surgeons and American Association of Neurological Surgeons.11
This resolution underscores how specialty societies and individual surgeons can engage in grassroots advocacy through state and national organizations to shape policy at the payer and legislative levels. Concurrently, the committee has launched a formal investigation evaluating the performance and limitations of AI models in prior authorization tasks, with the goal of informing evidence-based governance rather than unchecked adoption. Through coordinated policy development, empirical investigation, and member-driven advocacy, organized surgery is increasingly positioned to guide the ethical, transparent, and accountable integration of AI into healthcare delivery.
The next phase of advocacy must extend beyond specialty societies and toward system-level adoption and enforcement of responsible AI governance. This evolution should include collaboration with stakeholders like hospital systems, accrediting bodies, payer organizations, and federal and state regulators to establish standards for transparency, auditability, and clinician oversight of AI-enabled prior authorization tools.
Moving the field forward will require sustained engagement, cross-specialty collaboration, and a commitment to translating policy into practice to ensure that AI serves as a tool to enhance, rather than obstruct, high-quality care.
