Healthcare Needs AI, AI Needs Causality

AI should be built on rigorous knowledge... Note: This is a follow-up to an earlier article on causal machine learning, "AI Needs More Why". 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].