7 Ways We ’re Screwing Up AI in Healthcare

BY LEONARD D’AVOLIO The healthcare AI space is frothy.  Billions in venture capital are flowing, nearly every writer on the healthcare beat has at least an article or two on the topic, and there isn’t a medical conference that doesn’t at least have a panel if not a dedicated day to discuss. The promise and potential is very real. And yet, we seem to be blowing it. The latest example is an investigation in STAT News pointing out the stumbles of IBM Watson followed inevitably by the ‘is AI ready for prime time’ debate. If course, IBM isn’t the only one making things hard on itself. Their marketing budget and approach makes them a convenient target. Many of us – from vendors to journalists to consumers – are unintentionally adding degrees to an already uphill climb. If our mistakes led to only to financial loss, no big deal. But the stakes are higher. Medical error is blamed for killing between 210,000 and 400,000 annually. These technologies are important because they help us learn from our data – something healthcare is notoriously bad at. Finally using our data to improve really is a matter of life and death. In that spirit, here’s a short but relevant list of mistakes we’d all benefit from avoiding. It’s curated from a much longer list of sometimes costly, usually embarrassing mistakes I’ve made during my dozen years of trying to make these technologies work for healthcare. Inconsistent references to…whatever we’re callin...
Source: The Health Care Blog - Category: Consumer Health News Authors: Tags: Tech Uncategorized AI Big Data IBM-Watson medical error Source Type: blogs