Abstract
Much attention has been focused on describing the utility of artificial intelligence (AI) applied to diabetic retinopathy data. It has been determined that there are ample opportunities for AI algorithms within medicine and that AI is even superior to what we can determine with the professional human eye. However, fewer studies actually have looked at a combined model, or rather, a collective intelligence approach of both human and computer/machine efforts. We attempt to describe and demonstrate the power of collective intelligence in the future of medicine and to offer ways to consider a more complementary approach to both humans and computers.
Highlights
Diabetic retinopathy is the number one cause of vision loss in the world
It is with this logic that a more optimistic and harmonious future of artificial intelligence (AI) with humans is likely to contribute far more to medicine than either alone
We have demonstrated that a collective intelligence and ensemble approach to labeling medical imaging data is superior to crowd ML or AI alone
Summary
Diabetic retinopathy is the number one cause of vision loss in the world. An estimated 275 million people in the world have diabetes mellitus, with about 10% having vision threatening diabetic retinopathy [1]. Groups of researchers at companies like Google’s DeepMind have worked on using complex AI architectures to build, train, and screen for disease with high sensitivity and specificity [2, 3]. Companies such as IDx have been approved by the FDA in 2018 to screen for diabetic retinopathy in primary care offices [4]. As ML methods improve, and AI technology becomes more accessible worldwide, integrating the technology within healthcare systems for practical use becomes a greater challenge
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