Assessing AI-generated smoking cessation advice for patient education in primary care
BMC Primary Care, cilt.27, sa.1, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 27 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1186/s12875-026-03360-z
- Dergi Adı: BMC Primary Care
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CINAHL, MEDLINE, Directory of Open Access Journals
- Anahtar Kelimeler: Artificial intelligence, Digital health, DISCERN, Patient education, Primary care, Readability, Smoking cessation
- Kocaeli Üniversitesi Adresli: Evet
Özet
Background: Artificial intelligence (AI) has emerged as a promising tool to support smoking cessation in primary care, particularly for populations underserved by traditional interventions. However, the quality of AI-generated smoking cessation advice remains understudied, especially in low-resource settings and among vulnerable groups such as adolescents. Objective: This study aims to evaluate AI-generated responses to smoking cessation questions for patient education in primary care, comparing different AI programs in terms of knowledge, readability, and quality. Methods: Ten publicly accessible AI programs were prompted in Turkish with 24 standardized, open-ended smoking cessation questions framed as a patient consultation. Two family medicine specialists independently assessed each response’s readability using Ateşman’s Readability Index, reliability using the DISCERN instrument, and accuracy and motivational interviewing quality using a bespoke rubric and OARS (Open questions, Affirmations, Reflections, Summaries) framework. Inter‐rater agreement was evaluated via intraclass correlation. Descriptive statistics were computed for readability scores, DISCERN ratings, and accuracy grades. Results: All AI programs provided at least partially correct answers to all questions. The average readability score was 54.90 (medium difficulty) according to Atesman’s Index. The mean DISCERN score was 66 ± 5.2, indicating excellent quality. Three AI programs incorporated core motivational interviewing skills. The most accurately answered question concerned e-cigarettes’ harm compared to traditional cigarettes, while medication advice was least evidence-based. Conclusions: Free AI chatbots deliver reliably accurate and moderately readable smoking cessation advice, supporting their potential role as patient education adjuncts in primary care—particularly for individuals with at least a high school education. Further research should compare AI-assisted versus clinician‐led interventions on smoking cessation outcomes.