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A multi-agent approach to neurological clinical reasoning
Moran Sorka1,2, Alon Gorenshtein1,3, Dvir Aran2,4
1AI Neurology Laboratory, Ruth and Bruce Rapaport Faculty of Medicine, Technion-Institute of Technology, Haifa, Israel.
PLOS Digital Health
|December 4, 2025
Summary
Large language models show promise in neurology, but a novel multi-agent system significantly improved performance on complex clinical reasoning tasks, outperforming retrieval-augmented generation.
Area of Science:
- Artificial Intelligence in Medicine
- Clinical Neurology Decision Support
- Large Language Model Evaluation
Background:
- Large language models (LLMs) demonstrate potential in medicine, but their specialized reasoning for clinical neurology requires systematic assessment.
- Neurological assessment involves complex cognitive processes like anatomical localization and temporal pattern recognition, crucial for board certification.
Purpose of the Study:
- To systematically evaluate the performance of various large language models (LLMs) on specialized neurological reasoning tasks.
- To compare the effectiveness of base LLMs, retrieval-augmented generation (RAG), and a novel multi-agent system for neurological assessment.
Main Methods:
- Developed a benchmark of 305 questions from Israeli Neurology Board Certification Exams, categorized by complexity.
- Evaluated ten LLMs, including base models, RAG-enhanced versions, and a multi-agent system designed to mimic specialized cognitive functions.
- Conducted external validation using 155 neurological cases from the MedQA dataset.
Main Results:
- OpenAI-o1 achieved the highest base performance (90.9%); specialized medical LLMs performed poorly (Meditron-70B at 52.9%).
- Retrieval-augmented generation (RAG) offered variable improvements, with significant gains for mid-tier models but limited impact on high-complexity questions.
- The multi-agent framework dramatically improved performance, especially for mid-range models (LLaMA 3.3-70B reached 89.2% accuracy), particularly on complex questions.
Conclusions:
- A structured multi-agent approach significantly enhances complex medical reasoning in neurology, outperforming RAG and base LLMs.
- The multi-agent system effectively addresses persistent neurological reasoning challenges, transforming inconsistent performance into uniform excellence.
- This approach offers promising directions for AI assistance in challenging clinical neurological contexts.
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