Academic, regulatory, and medicolegal frameworks for artificial intelligence in surgery: a narrative review

Artificial intelligence (AI) and machine learning (ML) are redefining surgical decision-making, yet standards for academic publications, regulatory oversight, and medicolegal liability remain underdeveloped. Editorial bodies have established disclosure expectations for the use of large language models in manuscript preparation and standardized reporting frameworks to guide systematic reporting of ML research. Institutional review boards and regulatory agencies are using risk-based frameworks to oversee research and commercialization of clinical ML models. While existing case law is limited and reveals legal standards that may disproportionately disadvantage surgeons with respect to liability arising from AI use, there are opportunities for physician advocacy to effect the equitable integration of AI into surgical practice. To address these evolving standards, this narrative review summarizes (1) the disclosure and results-reporting standards for publications involving AI, (2) the regulatory disposition for AI algorithms, including outcome prediction models, and (3) medicolegal liability implications for surgeons using AI systems as part of clinical care.