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”
I wrote recently about my experiences of the Pulse leadership and entrepreneurship training program for the blog of the UK BioIndustry Association (BIA). The Pulse course is organised jointly by the BIA and the Francis Crick Institute. I joined the three-day course, in its first year of operation, in 2018.
I felt that I benefited enormously from the course. I had left my postdoc position 3 months previously and I was researching ideas for setting-up a company. I subsequently took my learnings from Pulse and elsewhere, and established my first company Simmunology. So when I was contacted earlier this year I was particularly keen to write something and say thanks.
Continue reading “New article: My experience of the BIA Pulse accelerator”
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.
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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:
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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….
There are three basic business models in bioinformatics:
- A consultancy
- Licencing of insights
- Selling a tool
In the consultancy model, you are being paid for your time and expertise. The risk lies with the payer (employer) in this case. There is no guarantee that you will come up with anything useful. Therefore your margins are also low.
Continue reading “Basic Business Models for Bioinformatics”
I am beginning a new project this week, the topic is Causal Inference. This is something I have been reading about, and wrestling with, for quite some time. Now seems a good point to take some time out, form a project, and see what I can get done on the topic.
Continue reading “New Project: Causal Inference”