I have been working on medical AI, in some form or other, for most of my adult life. For the past 12 months I have taken the opportunity to pause from racing forwards with my own start-ups and to look again, partly as a researcher, at the tools at my disposal and their intended applications. What I have seen worries me.
Part of my efforts to improve things, have taken the form of a number of peer-reviewed scientific articles. A few more such articles are still under review or exist only as work-in-progress. Today I want to summarise the 5 greatest problems which I see facing medical AI systems. For some of them I think that there are clear mitigations. For others, I suspect that we will need to rethink the entire system.
Continue reading “5 Big Challenges Facing Medical AI”
A good entrepreneur has a canny intuition for their True North. I’ve heard this from many good investors.
Personally I’ve always believed it. One of the bases through which I judge my professional contacts is on their decision making ability. Some people seem to always make good choices. Others, faced only with good outcomes, somehow still manage to find a more painful outcome.
Continue reading “True North”
One year ago, I left the start-up where I had been working on an AI-driven companion to accompany patients through their cancer treatments.
When I left, I was deeply frustrated with the start-up environment surrounding AI in Healthcare. I was still convinced that AI could help in this space, but all I was seeing was teams going down what I considered to be the wrong paths.
Continue reading “Preprint Announcement – Guide to Regulating Medical AI”
Marco Schmidt, the CSO of Biotx.ai, and I have published an opinion article in the Pharma Boardroom online trade journal. We have been discussing the topic ever since my first keynote talk at the launch of the Charité Digital Health Forum in 2018 (a video of the same talk is available from PyData 2018).
We title the article: Pharma’s Data Problem.
Continue reading “New article: Pharma’s Data Problem”
Continue reading “What do I work on?”
Mathematical, statistical, AI/ML, whatever
Something I’ve struggled with on and off over the 20 years that I have been making mathematical models is explaining those models to others. I have tried to bring people along and develop their understanding. But mainly what I observed was that, some people just got it and others did not.
I have certainly improved my own skill at explaining. This comes down to having streamlined stories and simpler take-home messages. Telling a clearer story certainly improves my audiences’ self-satisfaction, but ultimately some of them get the whole message and others do not.
Continue reading “Modelling the Modeller”
I have mentioned, in the past, that I am a huge fan of Nate Silver. Something which he used to repeat quite frequently, on their podcast, is a sort of predictive modelling tautology:
The best prediction of the future is no change.Nate Silver [Paraphrasing]
This concept has even got a probabilistic and philosophical theory behind it. All other things being equal, over the long history of time, the next moment from now is not likely to be any different from right now. If we repeat this process often enough then we will be right more often than we are wrong. In essence, we are accepting that there is continuity (and perhaps causality) in our experience of the natural world. Political scientist David Runciman even explored the concept in his recent work of political theory.
I originally took this statement in the manner in which, I hope, it was intended. But behind every great phrase there is often an enticing problem. Thinking over this phrase has led me to realise that there are three basic types of predictive models and each one of them has a fundamentally different purpose and indeed parameterisation.
Continue reading “Predictive Models”
Since I managed to break my writers’ block on decision making models last week I want to follow-up with a brief discussion on the use of Narrative in presenting decision models to an audience.
In my first article on decision making models I emphasized that a model must serve a purpose. In explaining our models to others I want to highlight that there are two purposes behind explaining a model; the first is to convince the audience; the second is to convey insights into the model. This is the opposite ordering of how scientifically-trained modellers typically think about communicating results, but it is by far-and-away the prioritisation of most top scientific communicators around the world.
Continue reading “Narrative and Decision Models”
This is my third attempt, over the course of 9 months, to write this article. The first attempt foundered on my desire to go into detail on whether explanation or explanability is a good characteristic of a model or not. I confess, this was overly motivated by my personal frustration at having worked with somebody who, “never let the facts get in the way of a good story.” The second attempt got lost in a forest of anecdotes from previous projects. I was trying so hard to knit them together that I failed to make a point. Today, I want to focus on the single most important thing that I have learned about developing decision making models.
Continue reading “Decision Making Using Models 3.0”
I am asked quite often how I see Data Science in the biomedical industry. I have, of course, many answers each of which is context dependent. However one theme which I find frequently recurring is a sort of straw-man debate which seems to inherently attract technical practitioners.
The debate is usually structured as follows:
Continue reading “Data Science in Biomedical Industry”
How do you see the validation of medical AI products working in practice?
Answer: clinical trials, test-validation sets, blah, blah
But doesn’t this lead to enormous overheads?
Answer: yes, but there are shortcuts
But if you take these shortcuts then don’t you run the risk of running into costly failures when you finally run the clinical trials?
It goes on….