Helmholtz Gemeinschaft

Search
Browse
Statistics
Feeds

Evaluating large language model workflows in clinical decision support for triage and referral and diagnosis

[thumbnail of Original Article]
Preview
PDF (Original Article) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
1MB
[thumbnail of Supplementary Information]
Preview
PDF (Supplementary Information) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
1MB

Item Type:Article
Title:Evaluating large language model workflows in clinical decision support for triage and referral and diagnosis
Creators Name:Gaber, F., Shaik, M., Allega, F., Bilecz, A.J., Busch, F., Goon, K., Franke, V. and Akalin, A.
Abstract:Accurate medical decision-making is critical for both patients and clinicians. Patients often struggle to interpret their symptoms, determine their severity, and select the right specialist. Simultaneously, clinicians face challenges in integrating complex patient data to make timely, accurate diagnoses. Recent advances in large language models (LLMs) offer the potential to bridge this gap by supporting decision-making for both patients and healthcare providers. In this study, we benchmark multiple LLM versions and an LLM-based workflow incorporating retrieval-augmented generation (RAG) on a curated dataset of 2000 medical cases derived from the Medical Information Mart for Intensive Care database. Our findings show that these LLMs are capable of providing personalized insights into likely diagnoses, suggesting appropriate specialists, and assessing urgent care needs. These models may also support clinicians in refining diagnoses and decision-making, offering a promising approach to improving patient outcomes and streamlining healthcare delivery.
Source:NPJ Digital Medicine
ISSN:2398-6352
Publisher:Springer Nature
Volume:8
Number:1
Page Range:263
Date:9 May 2025
Official Publication:https://doi.org/10.1038/s41746-025-01684-1
PubMed:View item in PubMed

Repository Staff Only: item control page

Downloads

Downloads per month over past year

Open Access
MDC Library