ArticleBioengineering (Basel, Switzerland)2024
Using Large Language Models to Retrieve Critical Data from Clinical Processes and Business Rules.
Article in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Evaluation Frameworks for Clinical Foundation Models in Specific Tasks of Unstructured Medical Text Analysis: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- The role of artificial intelligence in hypertension management.Current opinion in nephrology and hypertension · 2026Review
- Benchmarking large language models for medical education: performance on the clinical laboratory technician qualification examination.Frontiers in medicine · 2026Article
- Scalable medication extraction and discontinuation identification from electronic health records using large language models.Journal of clinical epidemiology · 2026Article
- An External Validation Study on Two Pre-Trained Large Language Models for Multimodal Prognostication in Laryngeal and Hypopharyngeal Cancer: Integrating Clinical, Treatment, and Radiomic Data to Predict Survival Outcomes with Interpretable Reasoning.Bioengineering (Basel, Switzerland) · 2025Article
- Comparative performance of neurosurgery-specific, peer-reviewed versus general AI chatbots in bilingual board examinations: evaluating accuracy, consistency, and error minimization strategies.Acta neurochirurgica · 2025Article
- Enhancing Clinical Decision Support with Adaptive Iterative Self-Query Retrieval for Retrieval-Augmented Large Language Models.Bioengineering (Basel, Switzerland) · 2025Article
- Retrieval augmented generation for large language models in healthcare: A systematic review.PLOS digital health · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current clinical care relies heavily on complex, rule-based systems for tasks like diagnosis and treatment. However, these systems can be cumbersome and require constant updates. This study explores the potential of the large language model (LLM), LLaMA 2, to address these limitations. We tested LLaMA 2's performance in interpreting complex clinical process models, such as Mayo Clinic Care Pathway Models (CPMs), and providing accurate clinical recommendations. LLM was trained on encoded pathways versions using DOT language, embedding them with SentenceTransformer, and then presented with hypothetical patient cases. We compared the token-level accuracy between LLM output and the ground truth by measuring both node and edge accuracy. LLaMA 2 accurately retrieved the diagnosis, suggested further evaluation, and delivered appropriate management steps, all based on the pathways. The average node accuracy across the different pathways was 0.91 (SD ± 0.045), while the average edge accuracy was 0.92 (SD ± 0.122). This study highlights the potential of LLMs for healthcare information retrieval, especially when relevant data are provided. Future research should focus on improving these models' interpretability and their integration into existing clinical workflows.
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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.