Study of AI-Assisted Ophthalmic Decision-Making Using Imaging and Medical Records

What this Trial Is About

Visual impairments greatly impact quality of life, making early screening, diagnosis, and treatment of eye diseases essential. This research evaluates an AI-assisted medical decision support system that combines various types of patient data, including images and electronic health records, to improve diagnosis and treatment planning for ocular diseases. The study aims to enhance diagnostic accuracy, streamline clinical workflows, and offer personalized treatment options to improve patient outcomes. The study uses an AI system that integrates multimodal data such as fundus photos, OCT scans, and clinical records. This AI model employs deep learning to provide real-time, personalized diagnostic and prognostic support, predicting disease progression and treatment results. The evaluation involves patients both with and without ocular diseases from multiple medical centers, using retrospective data collected from their electronic health records. Participants have previously received treatment at several hospitals, and their records include ophthalmic images, medical notes, and examination results. Researchers will analyze these data to assess the AI systems diagnostic accuracy, sensitivity, specificity, and decision-making effectiveness over one year. They will also monitor system usability, user satisfaction, treatment adherence, and physician acceptance, aiming to improve clinical decision-making and patient care through this technology.

Ophthalmic Multimodal AI-Assisted Medical Decision-Making