Efficacy of Using Large Language Model to Assist in Diabetic Retinopathy Detection

Sun Yat-sen University

Status: Completed

Conditions: Diagnosis; Diabetic Retinopathy

Treatments: Other: A self-evlaution tool based on Large Language Model

Study type: Interventional

Funder types: Other

Identifiers

NCT05231174

2022KYPJ258

Details and patient eligibility

About

With the increase in population and the rising prevalence of various diseases, the workload of disease diagnosis has sharply increased. The accessibility of healthcare services and long waiting times have become common issues in the public health medical system, with many primary patients having to wait for extended periods to receive medical services. There is an urgent need for rapid, accurate, and low-cost diagnostic services.

Enrollment: 535 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion and exclusion criteria

Inclusion Criteria The study will include adults aged 18 years and above who have been diagnosed with Type 2 diabetes but have not previously been screened for DR. Participants must demonstrate good compliance with clinical examinations, and provide informed consent.

Exclusion criteria The study will exclude patients who have previously been diagnosed with DR, those who have recently undergone eye surgery, and those with other significant eye diseases that could potentially confound the results of DR screening. Individuals with ocular, auditory, or cognitive impairments that prevent the use of mobile phones or reading will also be excluded.

Trial design

Primary purpose: Other

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

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535 participants in 1 patient group

A self-evlaution tool based on Large Language Model

Experimental group

Description:

The self-evlaution tool, powered by a large language model, processes user queries through a comprehensive generation, decision, action, and safety framework to deliver optimal responses. The system's key features include retrieval-augmented in-context learning, which enhances the responses generated by sourcing information from reliable websites. It also incorporates a Guardrail module to mitigate potential harmful content in the responses by validating the content before delivery. Additionally, the system features a Self-checking memory module that maintains essential clinical characteristics across multi-turn dialogues, ensuring consistent and continuous interactions with users.

Treatment:

Other: A self-evlaution tool based on Large Language Model