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.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
SCREENING
Masking
SINGLE
Enrollment
172
Large language model completes the medical history collection and recommends examinations.
Zhongshan Ophthalmic Center, Sun Yat-sen University
Guangzhou, Guangdong, China
Medical History Collection Scoring
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.
Time frame: through study completion, up to 1 week.
Accuracy of Recommended Tests
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.
Time frame: through study completion, up to 1 week.
Duration of Medical History Collection
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.
Time frame: through study completion, up to 1 week.
Patient satisfaction
Collected through a questionnaire.
Time frame: through study completion, up to 1 week.
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