ArticleProceedings. IEEE International Conference on Healthcare Informatics2024
Enhancing Large Language Models for Clinical Decision Support by Incorporating Clinical Practice Guidelines.
Article in Proceedings. IEEE International Conference on Healthcare Informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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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.
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Who cites it
19 citing papers in PubMed.
- Benchmarking large language models for question answering on German clinical practice guidelines.BMJ health & care informatics · 2026Article
- Optimizing GPT-5 for Operation-Procedure-Code-Extraction from Operative Reports in Meningioma Surgery: Feasibility and Comparison of Context-Enhancements.Applied clinical informatics · 2026Article
- Korean Large Language Models for Medical Question Answering on Arthritis: Fine-tuning and Comparative Evaluation.Healthcare informatics research · 2026Article
- Neuro-symbolic LLM Integration in Clinical Medicine: A Systematic Review and Taxonomy.Research square · 2026Article
- Deep learning and generative AI for medical imaging and clinical decision support systems: a structured critical review.Frontiers in digital health · 2026Review
- Application of large language models as decision support tools in occupational health and safety management: a cohort study of industrial workers.Frontiers in public health · 2026Observational
- Comparing large language models and search engine responses to common orthodontic questions.PloS one · 2026Article
- Ophtimus-V2-Tx: a compact domain-specific LLM for ophthalmic diagnosis and treatment planning.Scientific reports · 2025Article
- Comparative Evaluation of Advanced Chunking for Retrieval-Augmented Generation in Large Language Models for Clinical Decision Support.Bioengineering (Basel, Switzerland) · 2025Article
- Current Perspectives on the Artificial Intelligence in Critical Care Medicine.Anesthesiology clinics · 2025Review
- Enhancing Clinical Decision Support with Adaptive Iterative Self-Query Retrieval for Retrieval-Augmented Large Language Models.Bioengineering (Basel, Switzerland) · 2025Article
- A Review of Large Language Models in Medical Education, Clinical Decision Support, and Healthcare Administration.Healthcare (Basel, Switzerland) · 2025Review
- Utilizing large language models for gastroenterology research: a conceptual framework.Therapeutic advances in gastroenterology · 2025Review
- Large language models for disease diagnosis: a scoping review.NPJ artificial intelligence · 2025Review
- Large language models in clinical nutrition: an overview of its applications, capabilities, limitations, and potential future prospects.Frontiers in nutrition · 2025Review
- Using Large Language Models to Retrieve Critical Data from Clinical Processes and Business Rules.Bioengineering (Basel, Switzerland) · 2024Article
- Neonatal retinal hemorrhage and retinopathy of prematurity: a critical review of current practices and future directions.Therapeutic advances in ophthalmologyReview
- Dynamic alignment of large language models for evidence-grounded heart failure decision support.Digital healthArticle
- Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Large Language Models (LLMs), enhanced with Clinical Practice Guidelines (CPGs), can significantly improve Clinical Decision Support (CDS). However, approaches for incorporating CPGs into LLMs are not well studied. In this study, we develop three distinct methods for incorporating CPGs into LLMs: Binary Decision Tree (BDT), Program-Aided Graph Construction (PAGC), and Chain-of-Thought-Few-Shot Prompting (CoT-FSP), and focus on CDS for COVID-19 outpatient treatment as the case study. Zero-Shot Prompting (ZSP) is our baseline method. To evaluate the effectiveness of the proposed methods, we create a set of synthetic patient descriptions and conduct both automatic and human evaluation of the responses generated by four LLMs: GPT-4, GPT-3.5 Turbo, LLaMA, and PaLM 2. All four LLMs exhibit improved performance when enhanced with CPGs compared to the baseline ZSP. BDT outperformed both CoT-FSP and PAGC in automatic evaluation. All of the proposed methods demonstrate high performance in human evaluation. LLMs enhanced with CPGs outperform plain LLMs with ZSP in providing accurate recommendations for COVID-19 outpatient treatment, highlighting the potential for broader applications beyond the case study.
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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 OpenQuestion graph.