Google DeepMind Links ID with Decision Process

Quite an interesting claim.   The use of deep learning methods to identify problems using data and then applying process embedded solutions.   Here in the area of medicine:  Diagnosing 3D retinal scans.  The method being more transparent than simple deep learning methods.   And much closer to addressing process models and applications.  Will this solve the 'black box' (non transparency) problem of neural AI?  To be seen, but I like the idea.

Google DeepMind might have just solved the “Black Box” problem in medical AI

Deep Mind’s study published last week in Nature Medicine, presenting their Artificial Intelligence (AI) product capable of diagnosing 50 ophthalmic conditions from 3D retinal OCT scans. Its performance is on par with the best retinal specialists and superior to some human experts.

This AI product’s accuracy and range of diagnoses are certainly impressive. It is also the first AI model to reach expert level performance with 3D diagnostic scans. From a clinical point-of-view, however, what is even more groundbreaking is the ingenious way in which this AI system operates and mimics the real-life clinical decision process. It addresses the “Black Box” issue which has been one of the biggest barriers to the integration of AI technologies in healthcare.

DeepMind’s AI system addressed the “Black Box” by creating a framework with two separate neural networks. Instead of training one single neural network to identify pathologies from medical images, which would require a lot of labelled data per pathology, their framework decouples the process into two: 1) Segmentation (identify structures on the images) 2) Classification (analyze the segmentation and come up with diagnoses and referral suggestions)  .... "