Development and Validation of a Large Language Model-based Myopia Assistant System
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 70
- 试验地点
- 1
- 主要终点
- Satisfaction level
研究概览
简要总结
Myopia is a rapidly growing global health concern, and there is an urgent need for advanced tools that can facilitate personalized healthcare strategies. Artificial intelligence (AI)-based solutions, such as large language models, offer robust tools for ophthalmic healthcare. In this study, investigators aim to validate a patient-centered Large Language Model (LLM)-based Myopia Assistant System with the following key objectives: 1) evaluate the ability of the LLM models to generate high-level reports and help self-evaluation of myopia for patients in primary care; 2) evaluate its performance in answering evidence-based medicine-oriented questions and improving overall satisfaction within clinics for myopic patients.
详细描述
Myopia is a rapidly growing global health concern particularly affecting children and adolescents. The progression of myopia can lead to severe complications such as myopic macular degeneration, significantly impacting visual acuity and quality of life. With the rising prevalence of myopia, there is an urgent need for advanced tools that can facilitate personalized healthcare strategies. Artificial intelligence (AI)-based solutions, such as large language models, offer robust tools for ophthalmic healthcare. Nevertheless, their effectiveness and safety in real clinical environments have not been fully explored.
In this study, investigators aim to validate a patient-centered Large Language Model (LLM)-based Myopia Assistant System with the following key objectives: 1) evaluate the ability of the LLM models to generate high-level reports and help self-evaluation of myopia for patients in primary care; 2) evaluate its performance in answering evidence-based medicine-oriented questions and improving overall satisfaction within clinics for myopic patients. The findings of this study will provide valuable insights for the application of the GPT model in the healthcare field, making a significant contribution to improving the accessibility and quality of medical services.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Other
- 盲法
- Single (Outcomes Assessor)
入排标准
- 年龄范围
- 6 Years 至 75 Years（Child, Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Outpatient participants aged 6 to
- •Participants who undergo ophthalmic examinations for medical purposes.
- •Participants who can produce clear ophthalmic images in both eyes.
- •No prior experience in research involving digital medicine
- •Agree to participate in this study with written informed consent
排除标准
- •Participants who are reluctant to participate in this study
- •Participants who are unable to understand the study.
- •Participants who have recently undergone ocular surgery or those with severe ocular conditions that may affect the interpretation of imaging results related to myopia evaluation (e.g., severe vitreous hemorrhage, cataracts, corneal leukoma, etc.) will be excluded from the study.
- •Participants with poor quality of ophthalmic images, including blurriness, artifacts, underexposure, or overexposure.
- •Other unsuitable reasons determined by the evaluators.
结局指标
主要结局
Satisfaction level
时间窗: Immediately after the outpatient clinic visit procedure
Participants satisfaction level of the clinical experience with or without the use of a patient-centered assistant system based on a large language model (LLM) was assessed. The total satisfaction score was reported using the questionnaire (Patient User Satisfaction Scale), which evaluated the participant satisfaction with the clinical experience and the effectiveness of resolving their own issues. The questionnaire was measured on a 5-point Likert scale, where 1 represents strongly disagree; and 5 represents strongly agree; with higher scores indicating greater satisfaction.
次要结局
- Whether participants adopt the myopia management advice from the physician(Immediately after the outpatient clinic visit procedure)