Chiang Michael F

Deep learning in ophthalmology: The technical and clinical considerations.

Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed.

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.

Foundational Considerations for Artificial Intelligence Using Ophthalmic Images.

The development of a bioethically sound foundation may be possible if it is based in the fundamental ethical principles of nonmaleficence, autonomy, and equity for considerations for the design, validation, and implementation for AI systems.

Smoking Is Associated with Higher Intraocular Pressure Regardless of Glaucoma: A Retrospective Study of 12.5 Million Patients Using the Intelligent Research in Sight (IRIS®) Registry.

Current smokers and past smokers have higher IOP than patients who never smoked. This difference is higher in patients with an underlying glaucoma diagnosis.

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.