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

Not Applicable

Completed

• Conditions: Diagnosis; Diabetic Retinopathy
• Interventions: Other: A self-evlaution tool based on Large Language Model

• Registration Number: NCT05231174
• Lead Sponsor: Sun Yat-sen University

Brief Summary

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.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2022-01-29
• Study First Submitted QC: 2022-01-29
• Study First Posted: 2022-02-09
• Study Start: 2023-05-01
• Primary Completion: 2023-07-30
• Study Completion: 2023-07-30
• Status Verified: 2024-01
• Last Update Submitted: 2024-01-17
• Last Update Posted: 2024-01-19

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 535

Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: SINGLE_GROUP

Arm && Interventions

Group	Intervention	Description
A self-evlaution tool based on Large Language Model	A self-evlaution tool based on Large Language Model	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.

Primary Outcome Measures

Name	Time	Method
AUROC of the self-evaluation tool	Immediately after using the chatbot	The performance of the self-evaluation tool is evaluated with accuracy with reference to the diagnostic labels by senior ophthalmologists based on fundus photos.

Trial Locations

Locations (1)

Zhognshan Ophthalmic Center, Sun Yat-sen University; 🇨🇳 Guangzhou, Guangdong, China