Related Experiment Video
Updated: Jan 16, 2026
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Readability, reliability, and quality of kyphosis-related information provided by artificial intelligence chatbots: A
1Department of Orthopedics, Firat University Hospital, Firat University, Elazig, Turkey.
Background:
Artificial intelligence (AI) chatbots are increasingly used for health information dissemination. However, their effectiveness depends on the clarity, reliability, and quality of the content they deliver. This cross-sectional study aimed to evaluate the readability and reliability of kyphosis-related information provided by six major AI chatbots: ChatGPT, Gemini, Copilot, Perplexity, DeepSeek, and Grok.
Methods:
We selected the top 10 kyphosis-related questions from Google's "People also ask" section and submitted them to each chatbot. Readability was assessed using FKGL, FKRS, GFOG, SMOG, CL, ARI, and LW indices. Quality and reliability were evaluated using the DISCERN tool, JAMA benchmark, Global Quality Score (GQS), Ensuring Quality Information for Patients (EQIP), and a kyphosis-specific content score (KSC). Statistical analyses were performed using the Kruskal-Wallis and Mann-Whitney U tests.
Results:
No statistically significant difference was found among chatbots in FKGL, FKRS, SMOG, ARI, or GFOG scores. However, Perplexity had significantly higher DISCERN and EQIP scores, indicating superior content quality. All chatbots presented content at a readability level higher than the AMA-recommended sixth-grade level. While AI models provided more comprehensive and up-to-date information than traditional web sources, their outputs remained challenging for the average patient to comprehend.
Conclusions:
AI chatbots offer promising tools for disseminating health information about kyphosis but require significant improvements in readability. Expert-reviewed and patient-centered refinements are necessary to ensure accessibility and safety in digital health communication.
