User Trust Modeling and Optimization of AI-Assisted Diagnosis Systems for Promoting Patients’ Mental Health
Juan Yu, Taoying Hu, Jiawei LvAbstract
Objective
Uncertainty about artificial intelligence-assisted diagnosis results brings severe medical psychological pressure to patients, triggers uneasy and skeptical mental states, and negatively affects their medical experience and overall mental health. It is urgent to construct a scientific user trust model for AI diagnosis systems. This study explores the influencing factors of patient trust, optimizes system application strategies, and aims to reduce patients’ medical psychological pressure and stabilize their positive mental states to effectively improve patients’ mental health.
Subjects and Methods
This study takes patients with AI-assisted diagnosis experience as research subjects. It adopts questionnaire surveys and structural equation modeling to construct a user trust evaluation model. It empirically analyzes the correlation between system performance, patient trust degree, medical psychological pressure fluctuation, real-time mental states and individual mental health level in intelligent diagnosis scenarios.
Results
System transparency, diagnostic accuracy and interaction experience are core factors affecting patient trust. Low user trust significantly increases patients’ anxiety psychological pressure and causes negative mental states. The optimized trust model effectively enhances patients’ recognition of AI diagnosis, relieves medical psychological pressure, and achieves significant improvement in patients’ mental health status.
Conclusions
Targeted optimization of AI-assisted diagnosis systems can effectively build stable patient trust. Scientific system improvement measures alleviate patients’ long-term medical psychological pressure, correct pessimistic and fluctuating mental states in medical treatment, protect patients’ psychological well-being, and provide new paths for intelligent medical services to promote public mental health.
Corresponding Author
Juan Yu, Maanshan Teacher's College, Maanshan, 243000, China.