MYCIN did not fail: https://www.ncbi.nlm.nih.gov/pubmed/480542 https://www.ncbi.nlm.nih.gov/pubmed/480542 From the abstract: "MYCIN received an acceptability r

MYCIN did not fail: https://www.ncbi.nlm.nih.gov/pubmed/480542 https://www.ncbi.nlm.nih.gov/pubmed/480542 From the abstract: "MYCIN received an acceptability rating of 65% by the evaluators; the corresponding ratings for acceptability of the regimen prescribed by the five faculty specialists ranged from 42.5% to 62.5%." So, better than the human experts considered individually. Expert systems are able to explain their reasoning, which is essential if they are to be used for diagnosis. Neural networks cannot. Deep learning and similar approaches might be useful in two areas though: interpretation of images, e.g. what's on this X-ray or ultrasound scan, or what type of rash is this; and undiscovered associations, e.g. are patients who were given drug X combined with drug Y for disease P more likely to get disease Q later on in life? Deep learning in medicine also has a downside even supposing it works: lots of patient records are required, and anonymous ones can be linked to they people they describe, so there's a confidentiality problem. PS I notice that you appear to be agreeing with my in your article: "This was the MYCIN project, and in spite of the excellent research results, it never made its way into clinical practice." and even refer to the same paper, so we're really just using different criteria for success/failure. One of the problems of expert systems was getting them adopted by end users. I don't see how using neural networks will be any different in that regard.