Artificial Intelligence in Orthopaedic and Trauma Surgery Education: Applications, Ethics, and Future Perspectives

Abstract

Artificial intelligence (AI) is redefining surgical education by enabling personalized, data-driven learning environments. In orthopaedic trauma surgery, a specialty defined by diagnostic complexity, time-sensitive decision making, and procedural precision, AI tools are uniquely positioned to enhance resident training. This narrative review explores the role of AI subfields-machine learning (machine learning), deep learning, computer vision, natural language processing, and generative AI-in orthopaedic education. Each technology supports distinct educational functions, from real-time performance tracking and image interpretation to examination simulation and feedback automation. We describe how machine learning and deep learning models can assess technical competence and predict skill progression, whereas computer vision and augmented reality technologies provide immersive simulation and motion analysis. Natural language processing enables documentation analysis and scenario-based teaching, and large language models like ChatGPT support interactive, case-based learning. Ethical concerns such as algorithmic bias, data governance, transparency, and cognitive over-reliance are also discussed. A systems-based framework is proposed to integrate these technologies into a closed-loop educational cycle, emphasizing adaptive learning and professional growth. AI is not a substitute for surgical mentorship, but a powerful amplifier of educational quality. Its thoughtful implementation can foster equity, efficiency, and innovation in orthopaedic trauma training-transforming how surgical competence is acquired, assessed, and advanced.

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