Evaluate the Performance of Large Language Models in Ophthalmologic Patient Consultation
Status
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Treatments
Details and patient eligibility
About
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.
Enrollment
Sex
Volunteers
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.
Trial design
Primary purpose
Allocation
Interventional model
Masking
172 participants in 2 patient groups
Trial contacts and locations
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Central trial contact
Haotian Lin; Mingjie Luo
Data sourced from clinicaltrials.gov
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