A Study on Ophthalmic Multimodal AI-Assisted Medical Decision-Making Based on Imaging and Electronic Medical Record Data

What this Trial Is About

Visual impairments can greatly reduce quality of life, making early screening, diagnosis, and treatment of eye diseases critical to prevent vision loss. This research aims to develop an AI-assisted medical decision support system that combines multiple types of patient data, including imaging and electronic medical records, using deep learning methods. The goal is to improve diagnosis accuracy, personalize treatment, and enhance outcomes for patients with ocular diseases. The study compares two groups: one receiving AI-assisted medical decision-making that uses integrated multimodal data, and another receiving traditional decision-making by physicians without AI support. Patients in both groups undergo standard ophthalmic exams. The AI system will provide recommendations to guide treatment and diagnosis in the intervention group, while the control group relies on physician judgment alone. Participants will be evaluated over two years with assessments including imaging, lab tests, and follow-up visits. Researchers will measure diagnostic accuracy through metrics like sensitivity, specificity, and false positive/negative rates, as well as patient satisfaction, system usability, and treatment adherence. Safety outcomes such as complication and recurrence rates will also be monitored, aiming to streamline clinical workflow and improve quality of life for those with eye diseases.

Ophthalmic AI-Assisted Medical Decision-Making