I think you are confusing a few issues regarding 'success'. As pointed out Mycin performed well, and since de Dombal's work in the late 60's we knew that comput

I think you are confusing a few issues regarding 'success'. As pointed out Mycin performed well, and since de Dombal's work in the late 60's we knew that computers could perform better than experts in specific clinical domains. Similarly, Internist-1 and other systems performed quite well. The block for them was integration into clinical workflows. The biggest barrier was getting structured data that machines could use to run the algorithms, not the lack of performance. Today, workflow integration issues still remain, there is still a lot of free text entered etc. However a more pervasive issue is the lack of outcome data against which to train. In other words, what are we optimising algorithms for? In many health care systems we capture raw data, e.g. observations and labs, but not patient outcomes that are meaningful (i.e. based on optimising patient utility vs some more easily captured data). The final issue for deep nets and ML is that these are descriptive models, they learn from experience, where as we know there is huge variation in practice and outcomes. In medicine we may want normative models based on best evidence, or some combination. And then there's integration with individual patient utilities.