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Learn more about how autonomous AI is changing the healthcare landscape.
Cahaba Medical Care Case Study
Parkview Health Case Study
Foundational Considerations for Artificial Intelligence Utilizing Ophthalmic Images
Publication: American Academy of Ophthalmology
Johns Creek (Primary Care) Case Study
Identifying Ethical Considerations for Machine Learning Healthcare Applications
Publication: The American Journal of Bioethics
A Framework to Evaluate Ethical Considerations with ML-HCA Applications—Valuable, Even Necessary, but Never Comprehensive
Blessing Health Case Study
Testing for Diabetic Retinopathy in Primary Care
University Medical Center Case Study
Automated and Computer-Assisted Detection, Classification, and Diagnosis of Diabetic Retinopathy
Publication: American Telemedicine Association
Practice Guidelines for Ocular Telehealth-Diabetic Retinopathy, Third Edition
Publication: Practice Guidelines for Ocular Telehealth-Diabetic Retinopathy, Third Edition
Validation of Automated Screening for Referable Diabetic Retinopathy With an Autonomous Diagnostic Artificial Intelligence System in a Spanish Population
Publication: Journal of Diabetes Science and Technology
Lessons Learned About Autonomous AI: Finding a Safe, Efficacious, and Ethical Path Through the Development Process
Publication: American Journal of Ophthalmology
Autonomous Artificial Intelligence in Diabetic Retinopathy: From Algorithm to Clinical Application
A pilot autonomous AI‐based DR screening in Poland
Publication: Acta Ophthalmologica
Diagnostic Accuracy of a Device for the Automated Detection of Diabetic Retinopathy in a Primary Care Setting
Publication: American Diabetes Association
Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices
Publication: Nature Digital Medicine
IDx-DR Pivotal Trial
Validation of automated screening for referable diabetic retinopathy with the IDx‐DR device in the Hoorn Diabetes Care System
Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning
Publication: Investigative Ophthalmology & Visual Science Volume 57, Issue 13
