Clinical trial NCT06607822: Development and Validation of a Large Language Model-based Myopia Assistant System
Official title: Development and Validation of a Large Language Model-based Myopia Assistant System
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Myopia
Large Language Model
Keywords: Myopia
Large language model
Assistance
Lead sponsor: The Hong Kong Polytechnic University
Sponsor class: OTHER
First posted: 2024-09-23
Enrollment: 70
Sex: ALL
Minimum age (years): 6
Maximum age (years): 75
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: OTHER

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.

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.

Eligibility criteria: 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.

Primary outcomes:
measure: Satisfaction level
timeFrame: Immediately after the outpatient clinic visit procedure
Secondary outcomes:
measure: Whether participants adopt the myopia management advice from the physician
timeFrame: Immediately after the outpatient clinic visit procedure
Other outcomes:
Locations:
city: Hong Kong
country: China
facility: The Hong Kong Polytechnic University
state: Hong Kong
Study officials:
affiliation: The Hong Kong Polytechnic University
name: Mingguang He, M.D, Ph.D
role: PRINCIPAL_INVESTIGATOR
References: