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AI chatbots generally provide accurate basic pain-related information. However, readability challenges, moderate information quality, and inconsistencies with clinical recommendations limit their use as independent sources for patient education and clinical decision-making.
A quick chatbot query about pain may deliver an answer in seconds—but is that answer easy to understand, reliable, and consistent with clinical guidelines? A comprehensive review examining artificial intelligence (AI)-generated pain information highlights important gaps that may influence its use in patient education and clinical practice.
The review examined studies issued between 2024 and 2025 evaluating pain-related responses generated by DeepSeek, Perplexity, ChatGPT, Gemini, and other large language models (LLMs). Investigators assessed the readability, reliability, quality, and clinical guideline adherence of AI-generated information to determine its suitability for pain-related patient education and clinical use. Using the Simple Measure of Gobbledygook (SMOG), Flesch Reading Ease, and Flesch-Kincaid Grade Level indices, readability was measured.
Information quality and reliability were evaluated via DISCERN, the Global Quality Score, the Journal of the American Medical Association (JAMA) Benchmark Criteria, and Ensuring Quality Information for Patients (EQIP). Responses were also compared with relevant national and international clinical practice recommendations. The findings showed that AI chatbots generally offered accurate basic information about pain.
However, most responses exceeded recommended readability levels for patient education, potentially making the information difficult for some patients to understand. Overall information quality and reliability were typically moderate, with shortcomings in explaining treatment risks, presenting alternative management options, and transparently identifying information sources. Although AI-generated responses illustrated acceptable alignment with broad clinical guideline principles, inconsistencies remained in diagnostic details and treatment sequencing.
These limitations emphasize the difference between offering generally accurate health information and delivering sufficiently reliable guidance for individual clinical decisions. The review identifies AI chatbots as potentially valuable supplementary tools for pain-linked information, rather than independent sources for primary patient education or clinical decision-making. Improving readability, strengthening source transparency, and ensuring human oversight are essential to support safer use of AI in pain management. Tailoring content to patients’ health literacy levels may also help make AI-generated health information more accessible and valuable.
Medical Journal of Western Black Sea
Artificial intelligence in pain: A comprehensive review
İlhan Celil Özbek et al.
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