There’s much to be excited about with artificial intelligence (AI) in healthcare: Google AI is improving the workflow of clinicians with predictive models for diabetic retinopathy [2], many new approaches are achieving expert-level performance in tasks such as classification of skin cancer [3], and others surpassing the capabilities of doctors — notably the recent report of DeepMind’s AI for predicting acute kidney disease, capable of detecting potentially fatal kidney injuries 48 hours before symptoms are recognized by doctors [4].
Yet medical practitioners and researchers at the intersection of machine learning (ML) and medicine are quick to point out these successes are not representative of the more nuanced, non-trivial challenges presented by medical research and clinical applications. These ML success stories (notably all deep learning) are disease prediction problems, learning patterns that map well-defined inputs to well-labeled outputs [5].
Domains where instinctive pattern recognition works powerfully are what psychologist Robin Hogarth termed “kind learning environments” [6]. Patterns repeat over and over, and feedback is usually rapid and accurate. Exemplary domains are chess or Go where pieces are moved in a discrete sequence with defined rules and boundaries. AI has dominated these domains, in 1997 with chess champion DeepBlue, and 2016 with AlphaGo.
Kind learning environments are where AI in medicine has shown successes. Datasets are relatively-structured and isolated, and tasks are clear and well-defined. Even so these domains are too difficult and complex for standard statistical methods. AI muscle (read: deep learning) is able to parse data for structure and patterns better than human experts ever could. Models can effectively address questions like what is the likelihood of a patient to reach mortality within 6 months?
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Date: August 20, 2019
Source: Forbes
