Deep learning research in medicine is a bit like the Wild West at the moment; sometimes you find gold, sometimes a giant steampunk spider-bot causes a ruckus. This has derailed my series on whether AI will be replacing doctors soon, as I have felt the need to focus a bit more on how to assess … Continue reading Do machines actually beat doctors? ROC curves and performance metrics
So, the big news in medical AI research is that the Stanford ML group under Andrew Ng has released a paper on chest x-ray interpretation that claims human performance at identifying pneumonia. First up, very cool! Second up, I have concerns. Vague and not so vague discomforts. So, after having a few days to wrap … Continue reading Quick thoughts on ChestXray14, performance claims, and clinical tasks.
Today I want to look at two papers which tell us something very useful about medical AI, particularly if we are trying to predict the future of medicine.
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 … Continue reading The End of Human Doctors – The Bleeding Edge of Medical AI Research (Part 2)
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. … Continue reading The End of Human Doctors – The Bleeding Edge of Medical AI Research (Part 1)
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 … Continue reading The End of Human Doctors – Understanding Regulation
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 … Continue reading The End of Human Doctors – Radiology Escape Velocity
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 … Continue reading The End of Human Doctors – Understanding Automation
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 … Continue reading The End of Human Doctors – Understanding Medicine
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 … Continue reading The End of Human Doctors – Introduction