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

Electronics, Journal Year: 2025, Volume and Issue: 14(5), P. 1028 - 1028

Published: March 5, 2025

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

Language: Английский

Practical Aspects of Using Large Language Models to Screen Abstracts for Cardiovascular Drug Development: Cross-Sectional Study

JMIR Medical Informatics, Journal Year: 2024, Volume and Issue: 12, P. e64143 - e64143

Published: Sept. 30, 2024

Abstract Cardiovascular drug development requires synthesizing relevant literature about indications, mechanisms, biomarkers, and outcomes. This short study investigates the performance, cost, prompt engineering trade-offs of 3 large language models accelerating screening process for cardiovascular applications.

Language: Английский