Chen Jimmy S

Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology.

These techniques have been used to improve ocular disease diagnosis, risk assessment, and progression prediction. Techniques such as supervised machine learning, deep learning, and natural language processing were most commonly used in the articles reviewed.

Development of an Open-Source Annotated Glaucoma Medication Dataset From Clinical Notes in the Electronic Health Record.

Annotated free-text clinical notes can be de-identified for upload as an open dataset.

Usability and Clinician Acceptance of a Deep Learning-Based Clinical Decision Support Tool for Predicting Glaucomatous Visual Field Progression.

A CDS tool can be designed to present AI model outputs in a useful, trustworthy manner that clinicians are generally willing to integrate into their clinical decision-making.

Review of Retinopathy of Prematurity Management in the Anti-VEGF Era: Evolving Global Paradigms, Persistent Challenges and Our AI-Assisted Future.

Alongside therapeutic advancements, artificial intelligence (AI) and telemedicine programmes have the potential to expand screening accessibility, particularly in underserved regions, and improve inter-observer variability, though challenges in implementation remain.

Ophthalmic care at an academic medical centre for patients who were incarcerated or in immigration detention.

Delays in care and visit attrition were substantial for patients who were incarcerated or in immigration detention. More work is needed to quantify outcomes and address disparities in care for these marginalised communities.

Discrepancies in Ophthalmic Medication Documentation for Glaucoma Patients.