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

arXiv: https://arxiv.org/abs/2405.01249 · HTML: https://mlsanity.com/paper/2405.01249/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 131 references that are in the map.
- First posted on arXiv 2 May 2024.

## Builds on (most cited first, 30 of 48)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning](https://mlsanity.com/paper/2308.08747/) (2023)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining](https://mlsanity.com/paper/2210.10341/) (2022)
- [Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine](https://mlsanity.com/paper/2311.16452/) (2023)
- [Large Language Models are Few-Shot Clinical Information Extractors](https://mlsanity.com/paper/2205.12689/) (2022)
- [OpenPrompt: An Open-source Framework for Prompt-learning](https://mlsanity.com/paper/2111.01998/) (2021)
- [Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health](https://mlsanity.com/paper/2306.10070/) (2023)
- [Diagnostic Reasoning Prompts Reveal the Potential for Large Language Model Interpretability in Medicine](https://mlsanity.com/paper/2308.06834/) (2023)
- [Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data](https://mlsanity.com/paper/2307.14385/) (2023)
- [Towards Interpretable Mental Health Analysis with Large Language Models](https://mlsanity.com/paper/2304.03347/) (2023)
- [Multimodal LLMs for health grounded in individual-specific data](https://mlsanity.com/paper/2307.09018/) (2023)
- [BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing](https://mlsanity.com/paper/2206.15076/) (2022)
- [LLM-empowered Chatbots for Psychiatrist and Patient Simulation: Application and Evaluation](https://mlsanity.com/paper/2305.13614/) (2023)
- [Large Language Models are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationales](https://mlsanity.com/paper/2312.07399/) (2023)
- [Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction](https://mlsanity.com/paper/2310.06239/) (2023)
- [HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural Language Processing](https://mlsanity.com/paper/2203.05061/) (2022)
- [PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning](https://mlsanity.com/paper/2211.01761/) (2022)
- [Clinical Prompt Learning with Frozen Language Models](https://mlsanity.com/paper/2205.05535/) (2022)
- [An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing](https://mlsanity.com/paper/2309.08008/) (2023)
- [Large Language Models for Biomedical Knowledge Graph Construction: Information extraction from EMR notes](https://mlsanity.com/paper/2301.12473/) (2023)
- [Artificial Intelligence for Health Message Generation: Theory, Method, and an Empirical Study Using Prompt Engineering](https://mlsanity.com/paper/2212.07507/) (2022)
- [From Beginner to Expert: Modeling Medical Knowledge into General LLMs](https://mlsanity.com/paper/2312.01040/) (2023)
- [Overview of the PromptCBLUE Shared Task in CHIP2023](https://mlsanity.com/paper/2312.17522/) (2023)

## Built on by (most cited first, 14 of 14)

- [BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models](https://mlsanity.com/paper/2411.15232/) (2024)
- [Quantifying and Mitigating Premature Closure in Frontier LLMs](https://mlsanity.com/paper/2605.15000/) (2026)
- [Rehabilitation Exercise Quality Assessment and Feedback Generation Using Large Language Models with Prompt Engineering](https://mlsanity.com/paper/2505.18412/) (2025)
- [Quantized Large Language Models in Biomedical Natural Language Processing: Evaluation and Recommendation](https://mlsanity.com/paper/2509.04534/) (2025)
- [HealthContradict: Evaluating Biomedical Knowledge Conflicts in Language Models](https://mlsanity.com/paper/2512.02299/) (2025)
- [Clinical trial cohort selection using Large Language Models on n2c2 Challenges](https://mlsanity.com/paper/2501.11114/) (2025)
- [Retrieval augmented generation based dynamic prompting for few-shot biomedical named entity recognition using large language models](https://mlsanity.com/paper/2508.06504/) (2025)
- [Prompting Large Language Models for Supporting the Differential Diagnosis of Anemia](https://mlsanity.com/paper/2409.15377/) (2024)
- [A Language Vision Model Approach for Automated Tumor Contouring in Radiation Oncology](https://mlsanity.com/paper/2503.14933/) (2025)
- [RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models](https://mlsanity.com/paper/2604.17725/) (2026)
- [Multi-Task Learning for Extracting Menstrual Characteristics from Clinical Notes](https://mlsanity.com/paper/2503.24116/) (2025)
- [A Multi-Stage Validation Framework for Trustworthy Large-scale Clinical Information Extraction using Large Language Models](https://mlsanity.com/paper/2604.06028/) (2026)
- [Evaluating Patient Safety Risks in Generative AI: Development and Validation of a FMECA Framework for Generated Clinical Content](https://mlsanity.com/paper/2605.04085/) (2026)
- [RASPER: Reward-Aligned Summarization of Clinical Notes for EHR Outcome Prediction](https://mlsanity.com/paper/2610.02979/) (2026)

## 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.