QUALITY, RELIABILITY, AND READABILITY OF TURKISH DIZZINESS INFORMATION: WEB SOURCES VERSUS GENERATIVE ARTIFICIAL INTELLIGENCE

Methods: This cross-sectional matched digital content analysis included 10 patient-oriented questions related to dizziness. For each question, one Turkish web-based response, one ChatGPT response, and one Gemini response were obtained, yielding 30 texts. Google searches were performed between March 14 and 17, 2026, and AI responses were collected on March 17, 2026. Readability was assessed using the Ateşman and Çetinkaya-Uzun formulas. Quality and reliability were evaluated with the DISCERN instrument. A structured 5-item accuracy/safety form was also used.

Results: Mean DISCERN scores were 60.2±2.2 for ChatGPT, 56.1±4.9 for Gemini, and 51.7±8.1 for web sources, with a significant overall difference among groups (p=0.001). Mean accuracy scores were 8.7±0.9 for ChatGPT, 8.2±1.5 for Gemini, and 7.8±1.9 for web sources; however, this difference was not statistically significant (p=0.212). Çetinkaya-Uzun readability scores differed significantly among groups (p=0.007), whereas no significant difference was found for Ateşman readability scores (p=0.122). Risky advice was identified only in one web-based text, whereas overly definitive language was observed only in one Gemini response.

Conclusion: Generative AI responses showed higher quality/reliability scores and more favorable readability values in some comparisons; however, no statistically significant differences were found in clinical accuracy or safety-related variables. Therefore, higher DISCERN or readability scores should not be interpreted as evidence of clinical superiority or patient-safety advantage. Both AI-generated and web-based sources should be interpreted cautiously, and clinicians should guide patients in critically evaluating digital health information. Keywords : Dizziness; DISCERN; Large Language Models; Patient Safety; Readability; Health Information