[Generative artificial intelligence in orthopedics and trauma surgery-Guidelines for responsible use in undergraduate and continuing education].

Authors

Stephanie Herbstreit, Paul Schwanitz von Keitz, Mohamed Ghanem, Ricarda Johanna Seemann

Published in

Zeitschrift fur Orthopadie und Unfallchirurgie. Nov 10, 2026. Epub Nov 10, 2026.

Abstract

Generative artificial intelligence (AI) has become an established part of medical education and postgraduate training. Large language models (LLMs), such as ChatGPT are widely used for information retrieval, structuring complex content, and preparing clinical cases-often in an informal manner, without didactic guidance and without a clear understanding of how these systems function. For orthopedics and trauma surgery (O&T), a specialty characterized by high decision density and direct responsibility for patient safety, the key question is therefore no longer whether generative AI is used, but how it should be integrated.
This article analyzes current patterns of use and highlights a central discrepancy: widespread adoption contrasts with heterogeneous levels of competence. Generative AI can support the structuring of learning processes, simulate elements of clinical reasoning, and assist educators in developing teaching materials. At the same time, LLMs generate responses on a probability-based foundation and do not perform their own evidence verification. Plausible but incorrect information, loss of clinical context, or bias may influence diagnostic reasoning and undermine evidence-based decision-making. In a safety-critical field such as O&T, this necessitates clear orientation and professional standards.
The core of the article is the development of a discipline-specific framework for the integration of generative AI into undergraduate and postgraduate education. This framework combines an understanding of how generative AI systems operate with didactic responsibility, quality assurance, and a clear delineation of medical decision-making authority. Generative AI is positioned consistently as a supportive tool, not as a substitute for clinical expertise. Particular emphasis is placed on structured prompting as a key competence. The ability to formulate precise clinical questions, make contextual information explicit, and critically compare AI-generated outputs with guidelines and scientific literature is described as an integral component of contemporary medical professionalism.
In addition, the article proposes a practice-oriented guideline for students, residents, and educators. Its aim is to transform the currently informal use of generative AI into a quality-assured, competence-based integration within existing curricula. Educators, trainers, and professional societies are identified as central actors who must shape responsible AI use through clear standards, transparency, and didactic framing.
Generative AI is already reshaping learning and training processes in O&T. The article frames this development as a professional task: how can AI-supported learning be structured in a way that systematically strengthens clinical judgment, guideline competence, and professional responsibility? The proposed framework provides concrete guidance for the competence-oriented and responsibly integrated use of generative AI in undergraduate and postgraduate education.

PMID:
43008791
Bibliographic data and abstract were imported from PubMed on 11 Nov 2026.

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