SUN Yan 1,2 , XU Shangqing 1,3 , MA Kexin 4 , GU Yu 4 , CAO Xiong 1 , HAN Xiaoyan 1 , JIN Hongying 1 , LI Huiling 1 , MA Xiaoli 1,5,6,7 , MA Minjie 1,4,5,6,7,8
  • 1. Department of Thoracic Surgery (National Clinical Key Specialty), The First Hospital of Lanzhou University, Lanzhou, 730000, P. R. China;
  • 2. School of Nursing, Lanzhou University, Lanzhou, 730000, P. R. China;
  • 3. Minimally Invasive Surgery Simulation Training Center, The First Hospital of Lanzhou University, Lanzhou, 730000, P. R. China;
  • 4. The First Clinical Medical School of Lanzhou University, Lanzhou, 730000, P. R. China;
  • 5. Gansu Thoracic Surgery Quality Control Center, Lanzhou, 730000, P. R. China;
  • 6. Gansu International Science and Technology Cooperation Base for Research and Application of Key Thoracic Surgery Technologies, Lanzhou, 730000, P. R. China;
  • 7. Gansu Talent Introduction Base for Integrated Application Research of Thoracic Artificial Intelligence and Smart Healthcare, Lanzhou, 730000, P. R. China;
  • 8. Internet Hospital Office, The First Hospital of Lanzhou University, Lanzhou, 730000, P. R. China;
MA Minjie, Email: maminjie24@sina.com
Artificial intelligence; patient education; voice cloning; large language models; randomized controlled trial
10.7507/1007-4848.202602049
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Objective To investigate the effect of an artificial intelligence (AI)-powered voice cloning education system based on the self-reference effect on patient outcomes, and to compare the educational effects of a physician's voice versus the patient's own voice. Methods A prospective, three-arm, parallel-group randomized controlled trial was conducted. A total of 150 thoracic surgery inpatients at the First Hospital of Lanzhou University from May to September 2025 were included and randomly assigned in a 1 : 1 : 1 ratio to a traditional education group (control group, n=50), a physician’s voice-cloned AI education group (intervention group 1, n=50), and a patient's own voice-cloned AI education group (intervention group 2, n=50). The primary outcome was the education content compliance rate, which was automatically assessed using the DeepSeek-R1 model. Secondary outcomes included knowledge mastery, educational satisfaction, treatment adherence, quality of life (SF-36), and psychological status (HADS). Results A total of 145 (96.7%) patients completed the trial. There were no significant differences in age [(54.2±10.1) years, (55.8±9.7) years, and (53.9±10.5) years, respectively] or sex distribution (male/female: 28/20, 26/22, and 27/22, respectively) among the three groups (all P>0.05). The immediate post-education content compliance rates of both AI intervention groups were significantly higher than that of the control group (P<0.001). The patient’s own voice-cloned AI education group was significantly superior to the physician's voice-cloned AI education group and the control group in terms of knowledge mastery at discharge, treatment adherence at the 1-month follow-up, and anxiety and depression scores at the 1-month follow-up (all P<0.05). Conclusion An AI-powered education model leveraging the self-reference effect throughpatient’s own voice cloning significantly improves patient outcomes. This approach demonstrates superior results in knowledge retention, treatment adherence, and psychological well-being compared to traditional methods and physician’s voice cloning, offering a new paradigm for personalized and scalable intelligent health education.

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