The End of Human Doctors – The Bleeding Edge of Medical AI Research (Part 2)

Today we continue looking at breakthrough medical deep learning research, and review a major paper from Stanford researchers that reports "dermatologist level classification of skin cancer", published in Janurary 2017. As a reminder, a major focus of this dive into the state of the art research will be barriers to medical AI, particularly technical barriers. This …

The End of Human Doctors – The Bleeding Edge of Medical AI Research (Part 1)

More than any other part of this blog series, what we talk about today will have the most impact on whether machines are going to replace doctors anytime soon. We are going to start exploring the cutting edge of research in medical automation. In the previous articles in this series, we simply assumed deep learning can automate medical tasks. …

The End of Human Doctors – Understanding Regulation

Today we are talking about medical regulation, which is the last part of our foundation. After this we will be able to assess current research and predict the future of medicine. If you don't know already, all medical systems, devices, and treatments are regulated. The level of oversight varies, but any technology which has direct impact on …

The End of Human Doctors – Radiology Escape Velocity

In a change of plans, today I am going to provide a bit of relief to the doctors and medical students reading this series. Instead of looking at regulation, I want to talk about the timelines of medical automation that came up last week. I'm going to flesh out those ideas a bit, because I think putting …

The End of Human Doctors – Understanding Automation

Last week we discussed how doctors perform medicine, and what parts of the process are worth automating. It turns out that deep learning is a very good match for some of the most time consuming (and therefore costly) parts of medicine: the perceptual tasks. We also saw that many decisions simply fall out of the perceptual …

The End of Human Doctors – Understanding Medicine

Last post I introduced the big question - "Are doctors going to be replaced by computers soon?" We also saw one possible answer: "Yes", which we are going to investigate in this series of articles. Over the next few posts we will start building a foundation for answering this question, by defining and exploring some of the …

The End of Human Doctors – Introduction

I have emerged, blinking, from the darkness of grant/paper writing purgatory (a.k.a December to March in Australia). It is time to get the blog going again, and to make up for the long gap in posts I'm going to start with the big one. The question I get every time I tell a colleague what I …

Why I just love dropping out of MOOCs

I love massive open online courses. I love everything about them. I love the format. I love the platforms. I love the teachers. I love the flexibility and the lack of friction. And I love dropping out of them. This picture turned up in my Twitter feed recently, from Class Central by way of the …

Predicting Medical AI in 2017

Welcome to 2017! What a blast 2016 was. It seemed like every day there was a new, massive breakthrough in deep learning research. It was also the year that the wider world really started to take notice. The media, professional groups, and the general public all climbed aboard the AI hype train in 2016. Governments commissioned …

The three phases of medical AI trials

In a recent blogpost I explored how to critically read medical artificial intelligence research, focusing on the relevance of these experiments to clinical practice. It has since struck me that we don't have a simple, clear way to discuss the idea that some studies are still a still a long way off use in the clinics, and others …