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Abstract / Synopsis

Robert T. Chang, MD, explains how aggregated real-world data will drive practice patterns and algorithms going forward.

Artificial intelligence (AI) is the subject of numerous articles that tout how well machines function better than human ophthalmologists, but why is this happening?

In April 2018, the first FDA approval of autonomous AI for detecting referable diabetic retinopathy (DR) from fundus photos, the LumineticsCore™ (formerly known as IDx-DR) camera system (IDx Technologies Inc.), legitimized and essentially “jumpstarted” AI in ophthalmology, according to Robert T. Chang, MD, associate professor at the Byers Eye Institute of Stanford University, Palo Alto, CA.

“The most important thing to understand if the technology will become widespread is how quickly doctors and patients will trust an AI system, such as understanding its strengths and limitations, and how easily the technology will be integrated into current eyecare workflows, especially in terms of liability and business models,” he said.

The FDA was careful in approving the first specific AI doctorless screening method for detecting DR in fundus images with a heavy emphasis on safety (what could be missed).

The LumineticsCore (formerly known as IDx-DR) breakthrough-device, prospective, multicenter trial included exacting requirements, such as specific camera type, single primary reason for DR screening, a narrow asymptomatic population not previously evaluated for DR, and specific minimal cutoffs for specificity and sensitivity to detect DR that exceeded mild disease, a threshold which likely would not result in a bad outcome given a false negative.