NCT06607822

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

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 70 (actual)
• Sponsor: The Hong Kong Polytechnic University · Academic / Other
• Sex: All
• Age: 6 Years – 75 Years
• Healthy volunteers: Accepted

Summary

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.

Detailed description

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.

Conditions

• Myopia
• Large Language Model

Interventions

Type	Name	Description
DEVICE	A patient-centered assistant system based on Large-Language Model (LLM)	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.

Timeline

• Start date: 2024-09-21
• Primary completion: 2024-10-26
• Completion: 2024-10-26
• First posted: 2024-09-23
• Last updated: 2025-03-13

Locations

1 site across 1 country: China