A Study on the Effectiveness of Al-Assisted Patient Health Education Using Voice Cloning and ChatGPT: A Prospective...

Traditional patient education often lacks personalization and engagement, potentially limiting knowledge acquisition and treatment adherence[1]. Advances in artificial intelligence (AI), including voice cloning technology and large language models such as ChatGPT, offer new opportunities to deliver personalized, scalable, and interactive health education[2-3]. However, evidence regarding the comparative effectiveness of different AI-based voice cloning strategies and the reliability of automated AI evaluation tools remains limited[4-5].

To evaluate the effectiveness of AI-assisted patient education integrating voice cloning and ChatGPT, to compare physician voice cloning with patient self-voice cloning, and to assess the reliability of ChatGPT as an automated evaluation tool for education outcomes.

A prospective, three-arm, parallel-group randomized controlled trial.A total of 180 hospitalized patients requiring standardized health education were recruited from a tertiary hospital. Inclusion criteria were: age ≥18 years, clear diagnosis requiring health education, clear consciousness, and voluntary participation with informed consent. Exclusion criteria were: severe hearing impairment, severe cognitive impairment, expected hospitalization <3 days, or prior participation in similar studies.Participants were randomly assigned (1:1:1) to receive (1) traditional education (control), (2) AI-assisted education using physician voice cloning, or (3) AI-assisted education using patient self-voice cloning. All groups received identical educational content with equal duration.The primary outcome was education content compliance, evaluated using ChatGPT-4 with validated prompts and verified by expert review. Secondary outcomes included knowledge retention, education satisfaction, treatment adherence, quality of life (SF-36), and psychological status (Hospital Anxiety and Depression Scale).Participants were randomly allocated using a computer-generated random sequence. Due to the nature of the intervention, participants were not blinded; outcome assessors and data analysts were blinded to group allocation.

Of 180 randomized participants, 174 (96.7%) completed the trial. Both AI-assisted groups demonstrated significantly higher education content compliance immediately after education compared with the control group (physician voice: 86.7 ± 7.3; self-voice: 92.5 ± 6.8 vs control: 73.2 ± 8.5; P < 0.001). The patient self-voice group showed superior knowledge retention before discharge, higher education satisfaction, and greater treatment adherence compared with both the physician voice and control groups (all P ≤ 0.02). At one-month follow-up, the self-voice group maintained improved adherence (Cohen's d = 0.74) and exhibited significantly lower anxiety and depression scores (all P ≤0.02), along with improved SF-36 quality-of-life domains. ChatGPT-based evaluations demonstrated high reliability compared with expert assessments (weighted κ = 0.87, 95% CI 0.82-0.91).

This study introduces an innovative patient education model integrating AI voice cloning and ChatGPT, representing a novel approach distinct from previous studies that primarily relied on standard text-to-speech or professionally recorded content. The key innovation lies in utilizing patients' own cloned voices for health education delivery, leveraging the self-reference effect to enhance learning outcomes. Compared with prior research focusing on clinician-narrated content, this study provides the first empirical evidence that self-voice education produces superior outcomes across multiple domains including compliance, satisfaction, and psychological well-being. These findings contribute to the field by establishing a theoretical and practical framework for personalized AI-driven patient education. In real-world clinical settings, this approach offers a scalable, cost-effective solution to enhance patient engagement, particularly valuable in resource-limited environments where individualized education is challenging to deliver.

Trial Registration: Chinese Clinical Trial Registry (ChiCTR2500101882); registration application initiated on January 15, 2025 and finalized on April 30, 2025, before participant enrollment began in May 2025.