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Evaluate the Performance of Large Language Models in Ophthalmologic Patient Consultation

NCT06824389 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 172

Last updated 2026-01-08

No results posted yet for this study

Summary

The intelligent image models lack an understanding of diagnostic and treatment logic, and have not considered textual information such as symptoms and signs. Large language models like ChatGPT, can learn medical knowledge, understand, and generate human natural language, offering new technologies for medical knowledge-based intelligent question answering and the creation of smart medical documents. Therefore, our team plan to verify large language models' feasibility and effectiveness in ophthalmology clinics for medical history collection and examination recommendations during consultations, comparing its performance with traditional methods.

Conditions

  • Non-emergency Ocular Diseases

Interventions

OTHER

Consultation Model of large language model in Ophthalmology Clinics

Large language model completes the medical history collection and recommends examinations.

Sponsors & Collaborators

  • Zhongshan Ophthalmic Center, Sun Yat-sen University

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
SCREENING
Masking
SINGLE
Model
PARALLEL

Eligibility

Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2025-05-10
Primary Completion
2025-06-10
Completion
2025-06-17

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06824389 on ClinicalTrials.gov