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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
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT06824389
Enrollment
172
Registered
2025-02-13
Start date
2025-05-10
Completion date
2025-06-17
Last updated
2026-01-08

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Non-emergency Ocular Diseases

Brief 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.

Interventions

OTHERConsultation Model of large language model in Ophthalmology Clinics

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

Sponsors

Zhongshan Ophthalmic Center, Sun Yat-sen University
Lead SponsorOTHER

Study design

Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
SCREENING
Masking
SINGLE (Outcomes Assessor)

Eligibility

Sex/Gender
ALL
Healthy volunteers
Yes

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.

Design outcomes

Primary

MeasureTime frameDescription
Medical History Collection Scoringthrough 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

MeasureTime frameDescription
Accuracy of Recommended Teststhrough 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.
Duration of Medical History Collectionthrough 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.
Patient satisfactionthrough study completion, up to 1 week.Collected through a questionnaire.

Countries

China

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026