mlsanityData to 7 Oct 2026

Prompt engineering paradigms for medical applications: scoping review and recommendations for better practices

arXiv 2405.01249 ↗First posted 2 May 2024cs.CL cs.LGcited by 13

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Abstract (arXiv)

Prompt engineering is crucial for harnessing the potential of large language models (LLMs), especially in the medical domain where specialized terminology and phrasing is used. However, the efficacy of prompt engineering in the medical domain remains to be explored. In this work, 114 recent studies (2022-2024) applying prompt engineering in medicine, covering prompt learning (PL), prompt tuning (PT), and prompt design (PD) are reviewed. PD is the most prevalent (78 articles). In 12 papers, PD, PL, and PT terms were used interchangeably. ChatGPT is the most commonly used LLM, with seven papers using it for processing sensitive clinical data. Chain-of-Thought emerges as the most common prompt engineering technique. While PL and PT articles typically provide a baseline for evaluating prompt-based approaches, 64% of PD studies lack non-prompt-related baselines. We provide tables and figures summarizing existing work, and reporting recommendations to guide future research contributions.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 48 of its 131 references that are in the map. First posted on arXiv 2 May 2024.

Builds on

48 of 131 references.

References · 48

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  3. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  4. 2023A Survey of Large Language Modelscited by 2,537
  5. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  6. 2022Large Language Models Encode Clinical Knowledgecited by 1,807
  7. 2023Capabilities of GPT-4 on Medical Challenge Problemscited by 630
  8. 2023An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuningcited by 622
  9. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 612
  10. 2022BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Miningcited by 531
  11. 2023Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicinecited by 319
  12. 2022Large Language Models are Few-Shot Clinical Information Extractorscited by 206
36 more
  1. 2021OpenPrompt: An Open-source Framework for Prompt-learningcited by 148
  2. 2023Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Healthcited by 118
  3. 2023Diagnostic Reasoning Prompts Reveal the Potential for Large Language Model Interpretability in Medicinecited by 83
  4. 2023Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Datacited by 66
  5. 2023Towards Interpretable Mental Health Analysis with Large Language Modelscited by 53
  6. 2023Multimodal LLMs for health grounded in individual-specific datacited by 48
  7. 2022BigBIO: A Framework for Data-Centric Biomedical Natural Language Processingcited by 36
  8. 2023LLM-empowered Chatbots for Psychiatrist and Patient Simulation: Application and Evaluationcited by 33
  9. 2023Large Language Models are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationalescited by 33
  10. 2023Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extractioncited by 32
  11. 2022HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural Language Processingcited by 26
  12. 2022PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learningcited by 22
  13. 2022Clinical Prompt Learning with Frozen Language Modelscited by 19
  14. 2023An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processingcited by 19
  15. 2023Large Language Models for Biomedical Knowledge Graph Construction: Information extraction from EMR notescited by 17
  16. 2022Artificial Intelligence for Health Message Generation: Theory, Method, and an Empirical Study Using Prompt Engineeringcited by 14
  17. 2023From Beginner to Expert: Modeling Medical Knowledge into General LLMscited by 14
  18. 2023Overview of the PromptCBLUE Shared Task in CHIP2023cited by 14
  19. 2023CliniDigest: A Case Study in Large Language Model Based Large-Scale Summarization of Clinical Trial Descriptionscited by 10
  20. 2023Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets: Cognitive Distortions and Suicidal Risks in Chinese Social Mediacited by 9
  21. 2022Automated Identification of Eviction Status from Electronic Health Record Notescited by 6
  22. 2023Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Needcited by 6
  23. 2022Decorate the Examples: A Simple Method of Prompt Design for Biomedical Relation Extractioncited by 5
  24. 2022Using Bottleneck Adapters to Identify Cancer in Clinical Notes under Low-Resource Constraintscited by 5
  25. 2023Is ChatGPT a Biomedical Expert? -- Exploring the Zero-Shot Performance of Current GPT Models in Biomedical Taskscited by 5
  26. 2023ChatGPT-HealthPrompt. Harnessing the Power of XAI in Prompt-Based Healthcare Decision Support using ChatGPTcited by 5
  27. 2024Autocompletion of Chief Complaints in the Electronic Health Records using Large Language Modelscited by 4
  28. 2023ODD: A Benchmark Dataset for the Natural Language Processing based Opioid Related Aberrant Behavior Detectioncited by 3
  29. 2023Enhancing Summarization Performance through Transformer-Based Prompt Engineering in Automated Medical Reportingcited by 3
  30. 2023Clinical Text Deduplication Practices for Efficient Pretraining and Improved Clinical Taskscited by 3
  31. 2021GraphPrompt: Graph-Based Prompt Templates for Biomedical Synonym Predictioncited by 2
  32. 2023Soft-prompt tuning to predict lung cancer using primary care free-text Dutch medical notescited by 2
  33. 2023Keyword-optimized Template Insertion for Clinical Information Extraction via Prompt-based Learningcited by 1
  34. 2023Large Language Models and Prompt Engineering for Biomedical Query Focused Multi-Document Summarisationcited by 1
  35. 2023ChatGPT and post-test probabilitycited by 1
  36. 2024Prompt-based mental health screening from social media textcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 7; 2026: 4.

13 papers.

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  1. 2024BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Modelscited by 28
  2. 2025Rehabilitation Exercise Quality Assessment and Feedback Generation Using Large Language Models with Prompt Engineeringcited by 3
  3. 2025Quantized Large Language Models in Biomedical Natural Language Processing: Evaluation and Recommendationcited by 3
  4. 2025HealthContradict: Evaluating Biomedical Knowledge Conflicts in Language Modelscited by 3
  5. 2026Quantifying and Mitigating Premature Closure in Frontier LLMscited by 3
  6. 2025Clinical trial cohort selection using Large Language Models on n2c2 Challengescited by 2
  7. 2024Prompting Large Language Models for Supporting the Differential Diagnosis of Anemiacited by 1
  8. 2025A Language Vision Model Approach for Automated Tumor Contouring in Radiation Oncologycited by 1
  9. 2025Retrieval augmented generation based dynamic prompting for few-shot biomedical named entity recognition using large language modelscited by 1
  10. 2025Multi-Task Learning for Extracting Menstrual Characteristics from Clinical Notescited by 0
  11. 2026A Multi-Stage Validation Framework for Trustworthy Large-scale Clinical Information Extraction using Large Language Modelscited by 0
  12. 2026RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Modelscited by 0
1 more
  1. 2026Evaluating Patient Safety Risks in Generative AI: Development and Validation of a FMECA Framework for Generated Clinical Contentcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs
  2. 2026Employing General-Purpose and Biomedical Large Language Models with Advanced Prompt Engineering for Pharmacoepidemiologic Study Design
  3. 2025A Multi-faceted Analysis of Cognitive Abilities: Evaluating Prompt Methods with Large Language Models on the CONSORT Checklist
  4. 2024A Systematic Review on Prompt Engineering in Large Language Models for K-12 STEM Education
  5. 2026Prompting language influences diagnostic reasoning and accuracy of large language models
  6. 2025AutoMedPrompt: A New Framework for Optimizing LLM Medical Prompts Using Textual Gradients
  7. 2023Enhancing Medical Task Performance in GPT-4V: A Comprehensive Study on Prompt Engineering Strategies
  8. 2026When Chain-of-Thought Backfires: Evaluating Prompt Sensitivity in Medical Language Models