Diabetic Retinopathy Detection Using Collective Intelligence

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.

Keywords

• artificial intelligence
• medicine
• diabetic retinopathy
• collective intelligence
• ensemble learning