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The aim of this study was to assess the accuracy and readability of the answers generated by large language model (LLM)-chatbots to common patient questions about low back pain (LBP). This cross-sectional study analysed responses to 30 LBP-related questions, covering self-management, risk factors and treatment. The questions were developed by experienced clinicians and researchers and were piloted with a group of consumer representatives with lived experience of LBP. The inquiries were inputted in prompt form into ChatGPT 3.5, Bing, Bard (Gemini) and ChatGPT 4.0. Responses were evaluated in relation to their accuracy, readability and presence of disclaimers about health advice. The accuracy was assessed by comparing the recommendations generated with the main guidelines for LBP. The responses were analysed by two independent reviewers and classified as accurate, inaccurate or unclear. Readability was measured with the Flesch Reading Ease Score (FRES). Out of 120 responses yielding 1069 recommendations, 55.8% were accurate, 42.1% inaccurate and 1.9% unclear. Treatment and self-management domains showed the highest accuracy while risk factors had the most inaccuracies. Overall, LLM-chatbots provided answers that were 'reasonably difficult' to read, with a mean (SD) FRES score of 50.94 (3.06). Disclaimer about health advice was present around 70%-100% of the responses produced. The use of LLM-chatbots as tools for patient education and counselling in LBP shows promising but variable results. These chatbots generally provide moderately accurate recommendations. However, the accuracy may vary depending on the topic of each question. The reliability level of the answers was inadequate, potentially affecting the patient's ability to comprehend the information.
Scaff et al. (Thu,) studied this question.