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

NCT06607822 | Completed

• 1 other identifier: HSEARS20240229009
• Study Type: interventional
• Target: 70
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Myopia; Large Language Model

Keywords

Myopia; Large language model; Assistance

Outcome Measures

Primary Outcomes (1)

• 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.

  Immediately after the outpatient clinic visit procedure

Secondary Outcomes (1)

• Whether participants adopt the myopia management advice from the physician

  Immediately after the outpatient clinic visit procedure

Study Arms (2)

Patient-centered assistant system

EXPERIMENTAL

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

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

Control group

NO INTERVENTION

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.

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.

Patient-centered assistant system

Eligibility Criteria

• Age: 6 Years - 75 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Outpatient participants aged 6 to 75.
• 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

You may not qualify if:

• 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.

Sponsors & Collaborators

• The Hong Kong Polytechnic University: lead

Study Sites (1)

The Hong Kong Polytechnic University

Hong Kong, Hong Kong, 000, China

Location

MeSH Terms

Conditions

Myopia; Helping Behavior

Condition Hierarchy (Ancestors)

Refractive Errors; Eye Diseases; Social Behavior; Behavior

Study Officials

• Mingguang He, M.D, Ph.D

  The Hong Kong Polytechnic University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Purpose: OTHER
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 11, 2024
• First Posted: September 23, 2024
• Study Start: September 21, 2024
• Primary Completion: October 26, 2024
• Study Completion: October 26, 2024
• Last Updated: March 13, 2025

Record last verified: 2025-02

Locations

CN (1)