ArticleJournal of medical Internet research2025
Prompt Engineering in Clinical Practice: Tutorial for Clinicians.
Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed.
- Ambient AI Scribes to Create Educational Feedback Notes for Medical Students: Randomized Trial.JMIR medical education · 2026Trial
- Large Language Models Approximate Inter-Expert Agreement in Glaucoma Suspect and Glaucoma Classification from Multimodal Data.Ophthalmology science · 2026Article
- Evaluating Injection Laryngoplasty Skills Using a Foundation Model: A Feasibility Study.The Laryngoscope · 2026Article
- Generative AI Engagement and Perceived Nutrition-Oriented Feeding Practices Among Urban Indonesian Mothers: Mixed Methods Study.JMIR pediatrics and parenting · 2026Article
- The Impact of Specific Prompt Engineering Techniques on the Readability of LLM-Generated Patient Materials in Gastroenterology and Hepatology.Digestive diseases and sciences · 2026Article
- Evaluating large language models for lay summaries of radiology reports using tailored prompting strategies and mixed-method assessment.PLOS digital health · 2026Article
- Comments on "Evaluating the Efficacy of Large Language Models in Generating Medical Documentation: A Comparative Study of ChatGPT-4, ChatGPT-4o, and Claude".Aesthetic plastic surgery · 2026Article
- Structured prompting as reusable clinical tools.EULAR rheumatology open · 2026Article
- AI-generated patient information leaflets for oral anticoagulants: quality, usability, readability, and reproducibility compared with FDA-referenced materials.International journal of clinical pharmacy · 2026Article
- Iterative Development of an AI-Assisted Data Extraction Tool for Literature Synthesis in Oncology.Cureus · 2026Article
- Artificial intelligence advancements for orthopaedic clinical reasoning: longitudinal assessment of newer models (ChatGPT-5, Grok-3, Gemini 2.5 Flash) compared to clinicians.Archives of orthopaedic and trauma surgery · 2026Article
- How Reliably Do Large Language Models Reproduce Vital Pulp Therapy Guidelines? A Mixed-Effects Evaluation of Guideline-Concordance and Error Directionality.Healthcare (Basel, Switzerland) · 2026Article
- Large Language Model Hallucinations in Spine Surgery: A Comparative Analysis of Clinician vs Patient-Level Prompts.Neurosurgery practice · 2026Article
- Comparative performance of large language models and Drugs.com versus Lexicomp for antiseizure medication drug-drug interactions: A cross-sectional study with iterative prompting analysis.Exploratory research in clinical and social pharmacy · 2026Article
- Shaping AI in Pelvic Floor Physiotherapy: The Impact of Role-Play Prompting on ChatGPT Response Quality.International urogynecology journal · 2026Article
- Automated Approaches of Text Simplification of Patient Education Materials: Scoping Review.Journal of medical Internet research · 2026Article
- The effects of multitype prompt engineering for large language models in hypertension treatment decisions.NPJ digital medicine · 2026Article
- Leveraging AI solutions for sustainable practice in pediatric radiology: a practical guide and an educational tool.Pediatric radiology · 2026Review
- Analysis of Large Language Model Decision Making in Hormone Receptor-Positive/Human Epidermal Growth Factor Receptor 2-Negative Early Breast Cancer.JCO clinical cancer informatics · 2026Article
- Evaluation of three artificial intelligence chatbots for generating clinical hematology multiple choice questions for medical students.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unlabelled: Large language models (LLMs), such as OpenAI's GPT series and Google's PaLM, are transforming health care by improving clinical decision-making, enhancing patient communication, and simplifying administrative tasks. However, their performance relies heavily on prompt design, as small changes in wording or structure can greatly impact output quality. This presents challenges for clinicians who are not experts in natural language processing (NLP). This tutorial combines prompt engineering techniques tailored for clinical use, covering methods like zero-shot prompting, one-shot prompting, few-shot prompting, chain-of-thought prompting, self-consistency prompting, generated knowledge prompting, and meta-prompting. We provide actionable guidance on defining objectives, applying core principles, iterative prompt refinement, and integration into interoperable electronic health record (EHR) systems. This framework helps clinicians leverage LLMs to improve decision-making, streamline documentation, and enhance patient communication while maintaining ethical standards and ensuring patient safety.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.