High Awareness, Low Trust: Medical Residents’ Readiness to Use ChatGPT in Clinical Practice in an LMIC Context

Background: Large language models such as ChatGPT are rapidly entering medical education and clinical practice, yet evidence on clinicians’ readiness to adopt these tools in low- and middle-income countries remains limited. This study aimed to assess medical residents’ awareness, trust, perceptions, and predictors of willingness to use ChatGPT in clinical settings in Iran. Methods: A cross-sectional survey was conducted among 651 medical residents at Mashhad University of Medical Sciences between December 2023 and March 2024. A validated Persian questionnaire assessed familiarity with AI, trust in ChatGPT, perceived benefits and risks, and willingness to use the tool. Descriptive statistics, Chi-square tests, and multivariable logistic regression were used for data analysis. Results: Awareness of ChatGPT was universal; however, trust remained limited. Most residents supported the use of ChatGPT for low-risk tasks such as general health information, while none endorsed its independent diagnostic use. Multivariable regression showed that trust in ChatGPT, belief in its potential to improve clinical outcomes, and higher AI familiarity were the strongest independent predictors of willingness to use the technology. Concerns related to accuracy, privacy, and legal responsibility remained the dominant barriers to clinical adoption. Conclusion: Iranian medical residents demonstrate cautious optimism toward ChatGPT, but limited trust and regulatory uncertainty restrict real-world use. Strengthening AI education, establishing institutional policies, and developing national regulatory frameworks are essential to enable the safe and effective integration of large language models into clinical training and practice.