NCT06824389

Evaluate the Performance of Large Language Models in Ophthalmologic Patient Consultation

Evaluate the Performance of Large Language Models in Ophthalmologic Patient Consultation: A Randomized Clinical Trial

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 172 (actual)
• Sponsor: Zhongshan Ophthalmic Center, Sun Yat-sen University · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Accepted

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

Type	Name	Description
OTHER	Consultation Model of large language model in Ophthalmology Clinics	Large language model completes the medical history collection and recommends examinations.

Timeline

• Start date: 2025-05-10
• Primary completion: 2025-06-10
• Completion: 2025-06-17
• First posted: 2025-02-13
• Last updated: 2026-01-08

Locations

1 site across 1 country: China