ArticleFrontiers in medicine2024
A guide to prompt design: foundations and applications for healthcare simulationists.
Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Performance of DeepSeek-R1 and ChatGPT-5 in the Generation of North American Spine Society Clinical Guidelines for Adult Vertebral Compression Fractures: Comparative Study.Journal of medical Internet research · 2026Article
- Performance of DeepSeek V3.2 and ChatGPT 5.1 in Musculoskeletal Triage and Differential Diagnosis of Outpatients With Low Back Pain: Multidimensional Comparative Study.Journal of medical Internet research · 2026Article
- Benchmarking large language models on persian surgical subspecialty board examinations: a comparative study of ChatGPT-4o, ChatGPT-5, and Gemini 2.5 Flash.Scientific reports · 2026Article
- Large language models in sports injury care: a comparative expert evaluation of GPT-4o and GPT-5.BMC sports science, medicine & rehabilitation · 2026Article
- Comparison of large language models for clinical scenario generation in medical education: a mixed-methods study.BMC medical education · 2026Article
- Generative AI in simulation debriefings: an exploratory study using the Team-FIRST framework and qualitative feedback from simulation experts and learners.Advances in simulation (London, England) · 2026Article
- AI-Driven Objective Structured Clinical Examination Generation in Digital Health Education: Comparative Analysis of Three GPT-4o Configurations.JMIR medical education · 2026Article
- Article
- Assessment of brachial plexus and upper-extremity peripheral nerve injuries at the anatomical level using multimodal generative models.Frontiers in surgery · 2026Article
- Summarization of Narrative Clinical Data of Inflammatory Bowel Disease With Foundational Large Language Models.Gastro hep advances · 2026Review
- Article
- Ethical implications of using general-purpose LLMs in clinical settings: a comparative analysis of prompt engineering strategies and their impact on patient safety.BMC medical informatics and decision making · 2025Article
- Assessing the power of AI: a comparative evaluation of large language models in generating patient education materials in dentistry.BDJ open · 2025Article
- Few-Shot Prompting with Vision Language Model for Pain Classification in Infant Cry Sounds.Proceedings. IEEE International Symposium on Computer-Based Medical Systems · 2025Article
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4 authors.
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Abstract
Large Language Models (LLMs) like ChatGPT, Gemini, and Claude gain traction in healthcare simulation; this paper offers simulationists a practical guide to effective prompt design. Grounded in a structured literature review and iterative prompt testing, this paper proposes best practices for developing calibrated prompts, explores various prompt types and techniques with use cases, and addresses the challenges, including ethical considerations for using LLMs in healthcare simulation. This guide helps bridge the knowledge gap for simulationists on LLM use in simulation-based education, offering tailored guidance on prompt design. Examples were created through iterative testing to ensure alignment with simulation objectives, covering use cases such as clinical scenario development, OSCE station creation, simulated person scripting, and debriefing facilitation. These use cases provide easy-to-apply methods to enhance realism, engagement, and educational alignment in simulations. Key challenges associated with LLM integration, including bias, privacy concerns, hallucinations, lack of transparency, and the need for robust oversight and evaluation, are discussed alongside ethical considerations unique to healthcare education. Recommendations are provided to help simulationists craft prompts that align with educational objectives while mitigating these challenges. By offering these insights, this paper contributes valuable, timely knowledge for simulationists seeking to leverage generative AI's capabilities in healthcare education responsibly.
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