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Exploring the Role of Artificial Intelligence in Enhancing Surgical Education During Consultant Ward Rounds

2025·0 Zitationen·SurgeriesOpen Access
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0

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6

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2025

Jahr

Abstract

Background/Objectives: Surgical ward rounds are central to trainee education but are often associated with stress, cognitive overload, and inconsistent learning. Advances in artificial intelligence (AI), particularly large language models (LLMs), offer new ways to support trainees by simulating ward-round questioning, enhancing preparedness, and reducing anxiety. This study explores the role of generative AI in surgical ward-round education. Methods: Hypothetical plastic and reconstructive surgery ward-round scenarios were developed, including flexor tenosynovitis, DIEP flap monitoring, acute burns, and abscess management. Using de-identified vignettes, AI platforms (ChatGPT-4.5 and Gemini 2.0) generated consultant-level questions and structured responses. Outputs were assessed qualitatively for relevance, educational value, and alignment with surgical competencies. Results: ChatGPT-4.5 showed a strong ability to anticipate consultant-style questions and deliver concise, accurate answers across multiple surgical domains. ChatGPT-4.5 consistently outperformed Gemini 2.0 across all domains, with higher expert Likert ratings for accuracy, clarity, and educational value. It was particularly effective in pre-ward round preparation, enabling simulated questioning that mirrored consultant expectations. AI also aided post-round consolidation by providing tailored summaries and revision materials. Limitations included occasional inaccuracies, risk of over-reliance, and privacy considerations. Conclusions: Generative AI, particularly ChatGPT-4.5, shows promise as a supplementary tool in surgical ward-round education. While both models demonstrated utility, ChatGPT-4.5 was superior in replicating consultant-level questioning and providing structured responses. Pilot programs with ethical oversight are needed to evaluate their impact on trainee confidence, performance, and outcomes. Although plastic surgery cases were used for proof of concept, the findings are relevant to surgical education across subspecialties.

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Artificial Intelligence in Healthcare and EducationSurgical Simulation and TrainingRadiology practices and education
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