Prompt-Eng: Healthcare Prompt Engineering: Revolutionizing Healthcare Applications with Precision Prompts
Abstract
Prompt Engineering has emerged as a pivotal technique in Natural Language Processing, providing a flexible approach for leveraging pre-trained language models. Particularly, a prompt is used to instruct the model to adopt the nature of given prompts, which became a well-adoptable approach in wide areas of domains. Yet, existing prompt-guided frameworks are experiencing various challenges, such as crafting prompts for specific tasks to achieve clarity and conciseness and avoid ambiguity, which requires time and computational resources. Further existing methods heavily rely on the extensive labelled datasets, yet many domain-specific challenges exist, particularly in healthcare. This study presents Prompt-Eng, a novel framework emphasizing its wide-ranging applications in healthcare, where we design precise prompts with positive and negative aspects; we hypothesize that designing prompts in pairs helps models to generalize effectively. We delve into the significance of quick design and optimization, highlighting its influence in shaping model responses. In addition, we explore the increasing demand for prompts that are aware of the context in multimodal data analysis and the incorporation of prompt engineering in new machine-learning approaches. The essence of our approach is in creating tailored prompts, which serve as instructive guidelines for the models during the prediction procedure. The proposed methodology emphasizes utilizing context-aware prompt pairs to facilitate interpreting and extracting healthcare information from a health corpus by models. The study uses the medical MIMIC-III \footnotehttps://physionet.org/content/mimiciii/1.4/ corpus to predict medicine prescriptions. The paper also explores visual and textual prompts for X-ray image analysis for pneumonia prediction on the MIMIC-CXR \footnote\urlhttps://physionet.org/content/mimic-cxr/2.0.0/ dataset. This approach stands out from existing methods by addressing challenges such as clarity, conciseness, and context awareness, thereby enabling improved interpretation and extraction of healthcare information from diverse data sources.
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A. Ahmed et al. propose enhancing low-parameter models for medical contexts through advanced prompting techniques, improving their accuracy and relevance.
“Similarly, A. Ahmed et al. propose enhancing low-parameter models for medical contexts through advanced prompting techniques, improving their accuracy and relevance [28].”
From Evaluating Medical Text Summaries Using Automatic Evaluation Metrics and LLM-as-a-Judge Approach: A Pilot Study (2025) · cites this paper as [28]
Cites
MIMIC-III, a freely accessible critical care database
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Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361 · cited 3× · see citing works
“To address this challenge, prompt engineering has emerged as a promising approach to enhance the performance and interpretability of AI models in healthcare applications [3–5].”
BackgroundMethodLiu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv · doi:10.48550/arxiv.2107.13586 · Cited by 4,352 · cited 3× · see citing works
“To address this challenge, prompt engineering has emerged as a promising approach to enhance the performance and interpretability of AI models in healthcare applications [3–5].”
BackgroundMethodRevolutionizing healthcare: the role of artificial intelligence in clinical practice
Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., et al. (2023) · BMC Medical Education · doi:10.1186/s12909-023-04698-z · Cited by 3,146 · see citing works
MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports
Johnson, A. E. W., Pollard, T. J., Berkowitz, S. J., Greenbaum, N. R., Lungren, M. P., Deng, C.-Y., Mark, R. G., & Horng, S. (2019) · Scientific Data · doi:10.1038/s41597-019-0322-0 · Cited by 1,816 · see citing works
Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey
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Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference
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Review of Large Vision Models and Visual Prompt EngineeringPreprint
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OpenPrompt: An Open-source Framework for Prompt-learning
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Ahmed, A., Xi, R., Hou, M., Shah, S. A., & Hameed, S. (2023) · IEEE Access · doi:10.1109/access.2023.3323574 · Cited by 154 · see citing works
Self-citationMED-Prompt: A novel prompt engineering framework for medicine prediction on free-text clinical notes
Ahmed, A., Zeng, X., Xi, R., Hou, M., & Shah, S. A. (2024) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2024.101933 · Cited by 36 · see citing works
Self-citationClinical Prompt Learning with Frozen Language ModelsPreprint
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Reasoning language models for more transparent prediction of suicide risk
McCoy, T. H., & Perlis, R. H. (2025) · BMJ Mental Health · doi:10.1136/bmjment-2025-301654 · Cited by 8 · see citing works
Vasilev, Y., Raznitsyna, I., Pamova, A., Burtsev, T., Bobrovskaya, T., Kosov, P., Vladzymyrskyy, A., Omelyanskaya, O., & Arzamasov, K. (2025) · Diagnostics · doi:10.3390/diagnostics16010003 · Cited by 5 · see citing works
“Similarly, A. Ahmed et al. propose enhancing low-parameter models for medical contexts through advanced prompting techniques, improving their accuracy and relevance [28].”
- Authors
- Ahmed, A., Hou, M., Xi, R., Zeng, X., & Shah, S. A.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Birmingham City University (GB)
- Venue
- Companion Proceedings of the ACM Web Conference 2024
- Publisher
- ACM
- Published
- 2024-05-13
- Pages
- 1329-1337
- OpenAlex
- W4396844283
- Cited by
- 22
- References
- 16
- Citations by year
- 2026: 3 · 2025: 17 · 2024: 2
- FWCI
- 6.35
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International World Wide Web Conference (WWW) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- prompt engineering, MIMIC-III, MIMIC-CXR, pneumonia prediction, X-ray image analysis, multimodal prompts, clinical information extraction, large language models
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