Cross-industry thematic analysis of generative AI best practices: applications and implications for surgical education and training

The emergence of generative artificial intelligence (AI) technologies has ushered in a new era of potential applications across various industries. The value of this technology has been increasingly recognized, with integration having the potential to revolutionize both training and practice. However, there is currently a lack of governance policies and best practices with this technology with respect to surgical education. We set out to redress this research gap by conducting a thematic analysis using Braun’s principles of generative artificial intelligence (AI) technologies’ policies, standards, and governance frameworks across multiple industries.

The methodology involved a thorough examination of policies, guidelines, and communications from nine selected industries, including insurance, finance, sports, legal, government, academic, airlines, pharmaceutical, and automotive sectors. Three experts with diverse industry backgrounds and with shared backgrounds in surgical education, conducted line-by-line coding of 42 documents, using both deductive and inductive approaches. This process identified key themes related to the uses, risks, and mitigants of generative AI.

The results revealed four primary risk categories: privacy, inaccuracy, equity, and intellectual property. Additionally, four risk mitigant categories emerged: regulation and monitoring, application of existing policies, accountability, and transparency. This qualitative summary provides a foundational framework to understand the landscape of generative AI best practices and guidelines in the context of surgical education.

We outline a framework to guide ethical consideration when integrating generative AI into surgical education. Use cases demonstrate how the framework may be used by decision-makers across 4 surgical education applications. The results of this study provide a preliminary basis of cross-industrial themes to guide best practices and governance standards for integration of generative AI into surgical education.