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

The Hong Kong Polytechnic University

Status: Completed

Conditions: Myopia; Large Language Model

Treatments: Device: A patient-centered assistant system based on Large-Language Model (LLM)

Study type: Interventional

Funder types: Other

Identifiers

NCT06607822

HSEARS20240229009

Details and patient eligibility

About

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.

Full 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.

Enrollment: 70 patients

Sex: All

Ages: 6 to 75 years old

Volunteers: Accepts Healthy Volunteers

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.

Trial design

Primary purpose: Other

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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70 participants in 2 patient groups

Patient-centered assistant system

Experimental group

Description:

Participants engaged in the outpatient clinic visit procedure with a patient-centered assistant system based on Large-Language Model (LLM) for 10 minutes.

Treatment:

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

Control group

No Intervention group

Description:

Participants engaged in the outpatient clinic visit procedure without the support of patient-centered assistant system based on Large-Language Model (LLM) or any similar artificial intelligence assistance for 10 minutes.