Advanced Prompt Engineering in Emergency Medicine and Anesthesia: Enhancing Simulation-Based e-Learning

Abstract

Medical education is rapidly evolving with the integration of artificial intelligence (AI), particularly through the application of generative AI to create dynamic learning environments. This paper examines the transformative role of prompt engineering in enhancing simulation-based learning in emergency medicine. By enabling the generation of realistic, context-specific clinical case scenarios, prompt engineering fosters critical thinking and decision-making skills among medical trainees. To guide systematic implementation, we introduce the PROMPT+ Framework, a structured methodology for designing, evaluating, and refining prompts in AI-driven simulations, while incorporating essential ethical considerations. Furthermore, we emphasize the importance of developing specialized AI models tailored to regional guidelines, standard operating procedures, and educational contexts to ensure relevance and alignment with current standards and practices. The framework aims to provide a structured approach for engaging with AI-generated medical content, allowing learners to reflect on clinical reasoning, critically assess AI-generated recommendations, and consider the potential role of AI tools in medical training workflows. Additionally, we acknowledge certain challenges associated with the use of AI in education, such as maintaining reliability and addressing potential biases in AI outputs. Our study explores how AI-driven simulations could contribute to scalability and adaptability in medical education, potentially offering structured methods for healthcare professionals to engage with generative AI in training contexts.

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