med j west black sea Medical Journal of Western Black Sea 2822-4302 2587-0602 Zonguldak Bülent Ecevit Üniversitesi 10.29058/mjwbs.1881313 Pain Ağrı Ağrıda yapay zekâ: Kapsamlı bir derleme Artificial intelligence in pain: A comprehensive review https://orcid.org/0000-0003-0508-8868 Özbek İlhan Celil Department of Physical Medicine and Rehabilitation, Derince Training Research Hospital, Health Sciences University, Kocaeli, Türkiye https://orcid.org/0000-0003-3425-313X Özduran Erkan Department of Physical Medicine and Rehabilitation, Division of Pain Medicine, Sivas Numune Hospital, Sivas, Türkiye https://orcid.org/0000-0002-2227-194X Hancı Volkan Department of Anaesthesiology and Reanimation, Dokuz Eylül University, School of Medicine, İzmir, Türkiye 04 29 2026 10 1 168 178 02 03 2026 04 14 2026 Copyright © 2017, Batı Karadeniz Tıp Dergisi 2017 Batı Karadeniz Tıp Dergisi

Ağrı, yüksek yaygınlığı ve yaşam kalitesi üzerindeki önemli olumsuz etkisi nedeniyle dünya çapında büyük bir halk sağlığı sorunudur. Yapay zekâ (YZ) tabanlı sohbet robotları, sağlıkla ilgili bilgilere erişmek için giderek daha fazla kullanılmaktadır; ancak, bu sistemler tarafından sağlanan ağrı ile ilgili bilgilerin okunabilirliği, kalitesi, güvenilirliği ve klinik uygulama kılavuzlarıyla uyumu hakkındaki kanıtlar sınırlı ve heterojendir. Bu derleme, ağrı alanında YZ tabanlı sohbet robotları tarafından üretilen yanıtları okunabilirlik, bilgi kalitesi, güvenilirlik ve klinik uygulama kılavuzlarına uyum açısından değerlendiren mevcut literatürü kapsamlı bir şekilde incelemeyi amaçlamıştır. 2024 ve 2025 yılları arasında yayınlanan ve YZ sohbet robotlarının ağrı ile ilgili sorulara verdiği yanıtları değerlendiren çalışmalar analiz edildi. ChatGPT, Gemini, Perplexity, DeepSeek ve diğer büyük dil modelleri değerlendirildi. Okunabilirlik, Flesch Okuma Kolaylığı, Flesch-Kincaid Sınıf Düzeyi ve SMOG endeksleri kullanılarak değerlendirilirken, bilgi kalitesi ve güvenilirliği DISCERN, JAMA Kriterleri, EQIP ve Küresel Kalite Puanı kullanılarak değerlendirildi. Klinik kılavuzlara uyum, ilgili ulusal ve uluslararası önerilerle karşılaştırmalar yoluyla incelendi. İncelenen çalışmalar, yapay zekâ modellerinin genel olarak ağrı hakkında doğru temel bilgiler sağladığını göstermiştir. Bununla birlikte, çoğu yanıt, hasta eğitim materyalleri için önerilen okunabilirlik seviyelerini aşmıştır. Bilgi kalitesi ve güvenilirliği genellikle orta düzeyde olarak değerlendirilmiş olup, tedavi riskleri, alternatif seçenekler ve kaynak şeffaflığı tartışmalarında eksiklikler bildirilmiştir. Klinik kılavuzlara uyum genel prensipler düzeyinde kabul edilebilir olsa da, tanısal ayrıntılarda ve tedavi sıralamasında tutarsızlıklar tespit edilmiştir. Yapay zekâ tabanlı sohbet robotları, ağrı ile ilgili bilgiler için destekleyici araçlar olarak potansiyel gösterse de, klinik karar verme veya birincil hasta eğitimi için bağımsız kaynaklar olarak mevcut kullanımları sınırlıdır. Bu sistemlerin güvenli kullanımı için insan gözetimi ve sağlık okuryazarlığına uygun içerik üretimi şarttır.

Pain is a major public health problem worldwide due to its high prevalence and substantial negative impact on quality of life. Artificial intelligence (AI)-based chatbots are increasingly used to access health-related information; however, evidence regarding the readability, quality, reliability, and alignment of pain-related information provided by these systems with clinical practice guidelines remains limited and heterogeneous. This review aimed to comprehensively examine the existing literature evaluating responses generated by AI-based chatbots in the field of pain with respect to readability, information quality, reliability, and adherence to clinical practice guidelines. Studies published between 2024 and 2025 that assessed AI chatbot responses to pain-related questions were analyzed. ChatGPT, Gemini, Perplexity, DeepSeek, and other large language models were evaluated. Readability was assessed using the Flesch Reading Ease, Flesch-Kincaid Grade Level, and SMOG indices, while information quality and reliability were evaluated using DISCERN, the JAMA Benchmark Criteria, EQIP, and the Global Quality Score. Adherence to clinical guidelines was examined through comparisons with relevant national and international recommendations. The reviewed studies demonstrated that AI models generally provide accurate basic information about pain. However, most responses exceeded the recommended readability levels for patient education materials. Information quality and reliability were typically rated as moderate, with reported deficiencies in the discussion of treatment risks, alternative options, and source transparency. Although adherence to clinical guidelines was acceptable at the level of general principles, inconsistencies were identified in diagnostic details and treatment sequencing. While AI-based chatbots show potential as supportive tools for pain-related information, their current use as independent sources for clinical decision-making or primary patient education is limited. Human oversight and the generation of health literacy-appropriate content are essential for the safe use of these systems.

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