Background: Tobacco smoking cessation is a complex process influenced by biological, psychological, and social factors. Despite the availability of effective cessation interventions, relapse remains common among smokers attempting to quit. This study aimed to identify factors associated with previous smoking relapse and nicotine dependence among smokers attending anti-smoking clinics in Abha City, Saudi Arabia. Methods: An analytical comparative cross-sectional study was conducted among 330 adult Saudi smokers attending the Abha Anti-smoking Clinic and the Erada Anti-smoking Clinic. Previous smoking relapse was defined as having one or more unsuccessful smoking cessation attempts after a period of abstinence. Data were collected using a structured interviewer-administered questionnaire covering sociodemographic characteristics, smoking history, nicotine dependence, withdrawal symptoms, motives for smoking cessation, and relapse-related factors. Nicotine dependence was assessed using the Fagerström Test for Nicotine Dependence (FTND). Descriptive statistics, chi-square tests, and multivariable logistic regression analyses were performed. Results: Most participants were male (84.2%) and aged 20–39 years (59.4%). Smoking initiation most commonly occurred between 15 and 20 years of age (68.8%), while 66.4% reported previous quit attempts. Nervousness (84.2%) and stress or tension (74.2%) were the most frequently reported withdrawal symptoms. High and very high nicotine dependence were reported by 26.1% and 14.2% of participants, respectively. High nicotine dependence (AOR = 1.39, 95% CI: 1.15–1.69, p = 0.001) and very high nicotine dependence (AOR = 1.62, 95% CI: 1.21–2.17, p < 0.001) were independently associated with greater odds of previous smoking relapse. Attendance at the Erada Anti-smoking Clinic was associated with lower odds of relapse (AOR = 0.52, 95% CI: 0.29–0.91, p = 0.021). Conclusions: Higher nicotine dependence was independently associated with previous smoking relapse among smokers attending anti-smoking clinics. Comprehensive cessation programs integrating behavioral counseling, pharmacological treatment, and sustained follow-up may improve long-term abstinence and reduce relapse.

Background/Objectives: Artificial intelligence (AI) chatbots are increasingly used for healthcare information retrieval, education, and clinical support; however, concerns remain regarding the accuracy and completeness of AI-generated information in preventive medicine, where evidence-based recommendations, epidemiological interpretation, risk assessment, and public health guidance require reliable responses. Comparative evidence across multiple preventive medicine domains remains limited, particularly using standardized expert-validated reference answers. This study aimed to evaluate and compare the accuracy and completeness of responses generated by five AI chatbots across major preventive medicine domains. Methods: A comparative cross-sectional benchmarking study evaluated ChatGPT, DeepSeek, Perplexity AI, Claude AI, and Gemini Advanced using 15 expert-validated questions covering General Preventive Medicine, Clinical Preventive Medicine, Epidemiology and Biostatistics, Occupational Medicine, and Social and Behavioral Science. Responses were independently evaluated by two investigators using predefined accuracy and completeness scoring frameworks. Between-model comparisons were conducted using mixed-effects models accounting for clustering by question, and inter-rater reliability was assessed using agreement statistics. Results: Claude AI achieved the highest overall accuracy (5.67 ± 0.41) and completeness (2.87 ± 0.35), followed by ChatGPT (accuracy, 5.47 ± 0.66; completeness, 2.67 ± 0.49), whereas Gemini Advanced demonstrated the lowest overall performance. Epidemiology and Biostatistics showed the highest pooled completeness score (2.60; 95% CI: 2.32–2.88). Mixed-effects analysis accounting for clustering by question demonstrated a significant overall difference in accuracy among chatbot models (p < 0.001), while inter-rater agreement was high, supporting the consistency of the scoring procedure. Conclusions: Substantial variability in accuracy and completeness was observed among the evaluated AI chatbots, with Claude AI and ChatGPT demonstrating comparatively stronger performance within this specific question set. Given the limited number of unique questions and the continuously evolving nature of AI models, these findings should be considered exploratory and should not be generalized as definitive evidence of model superiority or clinical utility. Larger studies using more diverse question sets and standardized evaluation frameworks are required to confirm these findings.