Development and Validation of a Large Language Model-based Myopia Assistant System

The Hong Kong Polytechnic University70 enrolled

Overview

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.

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.

Study Type

INTERVENTIONAL

Allocation

RANDOMIZED

Purpose

OTHER

Masking

Interventions

A patient-centered assistant system based on Large-Language Model (LLM)DEVICE

Participants will engage in a 10-minute medical consultation using LLM model interface embedded in a tablet device before their regular face-to-face consulation with physicians. During the trials, participants could engage in free conversations covering aspects including risk factors, symptoms, diagnosis, examinations, treatment, advice and caution, etc. Participants who have completed the ophthalmic imaging examination will be asked to input results into the assistant model to generate structured reports.

Eligibility

Sex: ALLMin age: 6 YearsMax age: 75 YearsHealthy volunteers:

Medical Language ↔ Plain English

Inclusion Criteria: 1. Outpatient participants aged 6 to 75. 2. Participants who undergo ophthalmic examinations for medical purposes. 3. Participants who can produce clear ophthalmic images in both eyes. 4. No prior experience in research involving digital medicine 5. Agree to participate in this study with written informed consent Exclusion Criteria: 1. Participants who are reluctant to participate in this study 2. Participants who are unable to understand the study. 3. 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. 4. Participants with poor quality of ophthalmic images, including blurriness, artifacts, underexposure, or overexposure. 5. Other unsuitable reasons determined by the evaluators.

Outcomes

Primary Outcomes

Satisfaction level

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.

Time frame: Immediately after the outpatient clinic visit procedure

Secondary Outcomes

Whether participants adopt the myopia management advice from the physician

It is a binary outcome that assesses whether participants follow the recommendations from the physician for myopia management. It focuses on whether participants implement the prescribed treatments or interventions provided to control or manage their myopia.