By LUKE OAKDEN-RAYNER, MBBS

I got asked the other day to comment for Wired on the role of AI in Covid-19 detection, in particular for use with CT scanning. Since I didn’t know exactly what resources they had on the ground in China, I could only make some generic vaguely negative statements. I thought it would be worthwhile to expand on those ideas here, so I am writing two blog posts on the topic, on CT scanning for Covid-19, and on using AI on those CT scans.

As background, the pro-AI argument goes like this:

CT screening detects 97% of Covid-19, viral PCR only detects 70%!A radiologist takes 5-10 minutes to read a CT chest scan. AI can do it in a second or two.If you use CT for screening, there will be so many studies that radiologists will be overwhelmed.

In this first post, I will explain why CT, with or without AI, is not worthwhile for Covid-19 screening and diagnosis, and why that 97% sensitivity report is unfounded and unbelievable.

Next post, I will address the use of AI for this task specifically.

I’ve been getting a bit upset

Anyone remember Film Critic Hulk? Someone should definitely do a Research Critic Hulk. PUNY SCIENTIST CLAIMS A P-VALUE OF 0.049 IS CLINICALLY MEANINGFUL? HULK SMASH!

I was initially going to write a single post on AI, but as I started reading the radiology literature around CT use in more depth, I found myself getting more and more frustrated. These articles, published in very good journals, are full of flawed designs and invalid conclusions!*

So I have split this post off, and written an article that isn’t about AI at all. 

I still think this is relevant for AI-interested readers though, since this is a great example of how a surface level reading of the literature can be really misleading. As always,

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