Generative Artificial Intelligence in Orthopedics and Trauma Surgery

Generative artificial intelligence (AI) and large language models, including ChatGPT, are increasingly utilized across undergraduate medical education and postgraduate surgical training for information retrieval, structuring complex concepts, and clinical case preparation. However, adoption frequently occurs informally without didactic framing or a foundational understanding of underlying model mechanics. In high-decision-density and safety-critical disciplines such as orthopedics and trauma surgery (O&T), an educational discrepancy has emerged between widespread technological adoption and heterogeneous user competence.

Because large language models generate outputs on probabilistic foundations without independent evidence verification, they carry risks of generating plausible yet factually incorrect information, omitting essential clinical context, or introducing bias into diagnostic and management considerations. To address these vulnerabilities, a discipline-specific framework and practice-oriented guidelines have been developed for students, resident trainees, and clinical educators. This framework combines operational literacy with quality assurance, didactic responsibility, and the explicit preservation of medical decision-making authority.

Within this framework, structured prompting is established as an essential clinical competency, requiring practitioners and trainees to frame precise clinical inquiries, detail context explicitly, and benchmark AI outputs against clinical practice guidelines and primary scientific literature. Medical educators, residency program directors, and professional surgical societies are tasked with formalizing this transition, establishing transparent curricular standards to ensure AI integration reinforces evidence-based clinical judgment and professional accountability.