Algorithmic de-skilling of clinical decision-makers

What will we do when we don’t drive most of the time but have a car that hands control to us during an extreme event? Agrawal, A., Gans, J. & Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Before I get to the takehome message, I need to set this up a […]

Prof Allan Dafoe on trying to prepare the world for the possibility that AI will destabilise global politics

…even if we stopped at today’s AI technology and simply collected more data, built more sensors, and added more computing capacity, extreme systemic risks could emerge, including: 1) Mass labor displacement, unemployment, and inequality; 2)The rise of a more oligopolistic global market structure, potentially moving us away from our liberal economic world order; 3)Imagery intelligence […]

Defensive Diagnostics: the legal implications of AI in radiology

Doctors are human. And humans make mistakes. And while scientific advancements have dramatically improved our ability to detect and treat illness, they have also engendered a perception of precision, exactness and infallibility. When patient expectations collide with human error, malpractice lawsuits are born. And it’s a very expensive problem. Source: Defensive Diagnostics: the legal implications […]

The first AI disruption in medicine might not be radiology

Over 90% of traffic accidents are caused by human error. Whether it is drink driving, inattention, speeding, straight up bad driving, or any other of a myriad of reasons why people crash their cars, the fact is humans are really dangerous on the road. In the USA we average an accident every 200,000 to 500,000 […]

I enjoyed reading (January)

This post is also a bit delayed, but I’m OK with that. During January I found myself reading a bit more than usual about robots, androids, augmented reality and related topics. I’m not sure why it worked out that way, but this collection is more or less representative of what I found interesting during that […]

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