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Large language models and conditional rules in clinical decision support systems.

Shangeetha Sivasothy1, Adrian Bingham1, Irini Logothetis1

  • 1Applied Artificial Intelligence Initiative, Deakin University, Geelong, VIC Australia.

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|January 26, 2026
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) and large reasoning models (LRMs) can generate initial rule sets for clinical decision support systems (CDSS), but LLMs show limitations in complexity and reasoning. LRMs offer improved effectiveness for CDSS rule generation, potentially reducing clinician and developer time.

Keywords:
Clinical decision support systemsConditional rulesLarge language models

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Clinical Decision Support Systems
  • Natural Language Processing

Background:

  • Clinical Decision Support Systems (CDSS) enhance medical decisions, improving patient outcomes and healthcare sustainability.
  • Developing CDSS rules is costly due to iterative clinician-developer feedback loops, impacting clinician availability for patient care.

Purpose of the Study:

  • To evaluate the effectiveness of Large Language Models (LLMs) and Large Reasoning Models (LRMs) in generating triaging rule sets for CDSS.
  • To compare AI-generated rule sets against a clinician-developed rule set for COVID-19 patient monitoring.

Main Methods:

  • Prompting various LLMs (GPT-3.5, GPT-4, GPT-4o, Gemini, Claude 3.5 Sonnet) and LRMs (GPT-o1-mini, Grok-4, Claude 4 Sonnet) using diverse prompting techniques.
  • Evaluating rule set effectiveness based on accuracy, interpretability, and rule complexity, comparing against the Pandemic Intervention Monitoring System (PiMS) CDSS.

Main Results:

  • LLMs generated screening rules instead of triaging rules unless PiMS variables were specified; resulting rules had lower interpretability and complexity, with accuracy ranging from 31.62% to 70.71%.
  • LRMs demonstrated varied interpretability (3-94 vs. PiMS' 41) and achieved higher accuracy (31.62% to 81.70%) compared to LLMs.

Conclusions:

  • LLMs are limited in emulating clinical rule sets due to simplicity and lack of complex reasoning.
  • LRMs show improved effectiveness but still have limitations; both LLMs and LRMs can provide feasible initial rule sets, reducing development time.
  • Future research should explore integrating LLMs and LRMs with decision trees to enhance CDSS rule set effectiveness.