Three things I know: Future-casting 2 of 3

Last week I published an article about Artificial General Intelligence. This week I want to follow-up with my second of three attempts to predict the future. As I said, last week, this was part of a game which is commonly played in incubators when trying to draw insights from deep-tech founders. This week I want to talk about the Virtual Patient for drug development

I founded my first company over three years ago. We made no secret of our interest in using in-silico methods to build a Virtual Patient for drug development. We didn’t succeed that time but our lack of success had little to do with either technical issues or a lack of a commercialisation option, it was entirely our own fault.

The Virtual Patient

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5 Big Challenges Facing Medical AI

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.

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True North

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.

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Data Science in Biomedical Industry

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:
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….

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RCTs vs Real-World Evidence for medical AI

Randomised controlled trials (RCTs) have been the gold standard for statistical evidence, of treatment effect, for over 100 years. Their strength is in their attempt to avoid major sources of bias in a comparison of the evidence. However, they are costly to run, particularly in the domain of personalised medicine, to which medical AI products typically belong.

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Invited Speaker: Dynamics of Immune Responses

I have been invited to speak at the Dynamics of Immune Responses workshop/seminar/conference in May-June 2020. The invitation arose through my previous efforts to found a company in this space.

There is a growing awareness in the field of immunology of the potential for using mathematical techniques. The wedge-issue here is the cascade of data appearing via new cytometry techniques; large-data looks like a math issue to most people. I of course come from the other side of a spectrum – everything looks like a math issue to me – I wanted to stimulate drug development which engages with immune system dynamics by founding my company.

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Build – Test – Move

First a mea culpa, I have a huge backlog of relatively heavy articles that I really want to add to the blog. But I’ve been busy getting married – congratulations to me – and I didn’t have enough time. I strongly believe in following relatively strict guidelines on writing and editing articles, where I set myself deadlines and avoid over-writing on topics – it is just a blog after all – but for deep insights I do also have a minimum standard that I want to be able to produce before I’m willing to hit the Publish button.

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