ObjectiveTo 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. MethodsA 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). ResultsA 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). ConclusionAn 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.
Neuroblastoma (NB) is the most common extracranial solid malignant tumor in children. NB has various clinical manifestations, many of which are not specific, which ultimately lead to the delayed diagnosis of the tumor. In order to provide guidance for the identification of paediatric NB, the guideline for the identification and referral of suspected paediatric neuroblastoma is formulated and complied using a standard formulation process, and has received input from multidisciplinary experts, based on existing evidence, clinical practices and China's national conditions.
