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OpenTrials
Completed

NCT Number: NCT06824389

Evaluate the Performance of Large Language Models in Ophthalmologic Patient Consultation

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.

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Key information

Conditions

Non-emergency Ocular Diseases

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Zhongshan Ophthalmic Center, Sun Yat-sen University

Guangzhou, Guangdong, China

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • No age or gender restrictions for patients.
  • Non-emergency ocular diseases including corneal diseases, lens disorders, and vitreoretinal diseases.
  • Voluntary participation with signed informed consent.

Exclusion criteria

  • Top 10 Ocular Emergencies: globe perforation, ocular chemical injury, corneal ulcer perforation, Pseudomonas aeruginosa keratitis, acute angle-closure glaucoma, acute panophthalmitis, central retinal artery occlusion, acute optic neuritis, endophthalmitis, and orbital cellulitis.

Treatment and study plan

Consultation Model of large language model in Ophthalmology Clinics

Other

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

Primary outcomes

  1. Medical History Collection Scoring

    Time frame: through study completion, up to 1 week.

    The medical history collection is performed using the standard outpatient medical record form. The scoring criteria are developed collaboratively by clinical doctors from multiple specialties and researchers. Scoring is independently conducted in a blinded manner by higher-level specialists.

Secondary outcomes

  1. Accuracy of Recommended Tests

    Time frame: through study completion, up to 1 week.

    The gold standard for both the experimental and control groups consists of test items independently selected by senior specialists, who are not involved in the study.

  2. Duration of Medical History Collection

    Time frame: through study completion, up to 1 week.

    The experimental group uses the developed system to record the consultation and medical record writing completion times, while the control group records the consultation time through audio recording and the medical record writing completion time through the developed system.

  3. Patient satisfaction

    Time frame: through study completion, up to 1 week.

    Collected through a questionnaire.

Sponsors and collaborators

Lead sponsor

Zhongshan Ophthalmic Center, Sun Yat-sen University

Other

Registry information

Official study title

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

Important dates

Study start
2025
Primary completion
2025
Study completion
2025
First posted
Feb 13, 2025
Registry last updated
Jan 8, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.