Artificial Intelligence in Neurology: Assessing the Diagnostic and Therapeutic Reasoning of Large Language Models
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View abstract on PubMed
Summary
This summary is machine-generated.Large language models (LLMs) show promise in diagnosing neurological conditions like Guillain-Barré syndrome (GBS). However, these AI tools require further development for personalized, long-term patient care alongside expert physician oversight.
Area Of Science
- Neurology
- Artificial Intelligence
- Medical Informatics
Background
- Artificial intelligence (AI) and large language models (LLMs) are increasingly used in medicine.
- Their role in clinical decision-making, especially in complex fields like neurology, is not fully understood.
- Neurological diagnoses present unique challenges due to overlapping syndromes and time-sensitive decisions.
Purpose Of The Study
- To evaluate the diagnostic and therapeutic reasoning capabilities of three leading LLMs.
- To assess AI performance in a complex neurological case involving Guillain-Barré syndrome (GBS) and spinal epidural lipomatosis (SEL).
Main Methods
- Three LLMs (ChatGPT 4.0, Google Gemini, Claude 3.5 Sonnet) were prompted with an identical complex neurological case.
- Outputs were assessed by four blinded neurologists using a standardized rubric across diagnostic accuracy, differential diagnoses, intervention planning, and monitoring.
- Performance was evaluated based on descriptive reviewer assessments.
Main Results
- All LLMs correctly identified GBS and suggested guideline-consistent treatments (IVIG, plasmapheresis).
- Claude 3.5 Sonnet scored highest overall (18.5/20), followed by ChatGPT (17.5) and Google Gemini (17.25).
- Specific strengths varied: Gemini led in primary diagnosis, Claude excelled in differential diagnoses and intervention, and ChatGPT was strongest in monitoring.
Conclusions
- LLMs demonstrate significant potential in supporting neurological decision-making, offering accurate diagnoses and evidence-based treatment recommendations.
- Current LLMs exhibit limitations in personalizing care for specific patient conditions (like SEL) and in developing comprehensive long-term management strategies.
- Cautious integration of LLMs with physician expertise is essential for optimal patient outcomes in complex neurological cases.
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