Access to high-quality health information (HI) is critical for everyone involved in the research and management of medical conditions such as spinal cord injury (SCI). Recently, the use of Large Language Models (LLMs) through AI-based chatbots like ChatGPT has become increasingly integral to how people seek and consume HI. While LLMs have been evaluated in various clinical and health domains, there remains a notable gap in the literature regarding their use for SCI-specific questions. We conducted a narrative synthesis to identify the opportunities, challenges, and risks of using LLMs in SCI-related HI tasks, and to provide future direction for researchers, clinicians, and policymakers to better understand this fast-evolving landscape. We searched PubMed, Embase, and Google Scholar up to December 2025 and identified nine primary articles that investigated LLMs in the context of SCI-related queries. Our synthesis of the literature revealed that although there are promising results, these should be taken with caution due to mixed evidence for LLM’s capability to effectively answer SCI-related questions. In addition, the LLM outputs were challenging to read, typically requiring an education level equivalent to a college-level student (grades 14–15) to be adequately understood. We recognize that LLMs can serve as valuable tools for accessing HI in SCI. However, LLMs can also pose significant risks, including the spread of mis- or dis-information that may be inaccurate or even dangerous, which can mislead individuals and caregivers, potentially resulting in detrimental health outcomes. Finally, methodological rigour needs to be improved to produce higher levels of evidence.
Desai et al. (2026) studied this question.