Healthcare Needs AI, AI Needs Causality

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, many new approaches are achieving expert-level performance in tasks such as classification of skin cancer, 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.

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.

The full Forbes article can be viewed at this link.