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Published on: August 17, 2013
Can Large Language Models Answer Questions about Spinal Cord Injury? Risks, Challenges, and Opportunities-A Narrative
Rahul K Desai1, Simran Saggu1, Masha Panahi1
1School of Public Health Sciences, Faculty of Health, University of Waterloo, Waterloo, Canada.
Neurotrauma Reports
|August 1, 2026
Summary
Large Language Models (LLMs) show promise for accessing health information in spinal cord injury (SCI) research. However, caution is advised due to mixed evidence, readability challenges, and potential misinformation risks.
Area of Science:
- Health Informatics
- Artificial Intelligence
- Neuroscience
Background:
- High-quality health information (HI) is crucial for spinal cord injury (SCI) research and management.
- Large Language Models (LLMs) are increasingly used for accessing HI, but their application in SCI is under-explored.
Purpose of the Study:
- To identify opportunities, challenges, and risks of using LLMs for SCI-related HI tasks.
- To provide future directions for researchers, clinicians, and policymakers.
- To synthesize current literature on LLMs in the SCI domain.
Main Methods:
- A narrative synthesis approach was employed.
- Searches were conducted in PubMed, Embase, and Google Scholar up to December 2025.
- Nine primary articles investigating LLMs for SCI-related queries were identified.
Main Results:
- LLMs show potential for SCI HI access but require caution due to mixed evidence on effectiveness.
- LLM outputs often necessitate a college-level reading comprehension (grades 14-15).
- Significant risks include the potential spread of inaccurate or dangerous misinformation.
Conclusions:
- LLMs can be valuable tools for SCI health information access.
- Careful consideration of risks, including misinformation, is essential.
- Enhanced methodological rigor is needed to improve evidence quality for LLM applications in SCI.
