Using artificial intelligence techniques for early diagnosis of lung cancer in general practice

Today, we’re speaking to Professor Martijn Schut, Professor of Translational AI in Laboratory Medicine and Professor Henk CPM van Weert, GP and Emeritus Professor of General Practice, both based at Amsterdam University Medical Center.

Title of paper: Artificial intelligence for early detection of lung cancer in GPs’ clinical notes: a retrospective observational cohort study

Available at: https://doi.org/10.3399/BJGP.2023.0489

In most cancers, the prognosis depends substantially on the stage at the start of therapy. Therefore, many methods have been developed to enhance earlier diagnosis, for example, logistic regression models, biomarkers, and electronic-nose technology (exhaled volatile organic compounds). However, as most patients are referred by their GP, who keeps life-long histories of enlisted patients, general practice files might contain hidden information that could be used for earlier case finding. An algorithm was developed to identify patients with lung cancer 4 months earlier, just by analysing their files. Contrary to other methods, all medical information available in general practice was used.

Transcript

This transcript was generated using AI and has not been reviewed for accuracy. Please be aware it may contain errors or omissions.

Speaker A

00:00:01.600 - 00:00:55.370

Hello and welcome to BJGP Interviews. I'm Nada Khan and I'm one of the associate editors of the journal. Thanks for taking the time today to listen to this podcast.

Today we're speaking to Professor Martin Schutt, who is a professor in translational AI and Laboratory medicine, and Professor Hank Vanwort, GP and Emeritus professor in General Practice, who are both based at Amsterdam University Medical Center. We're here to discuss their paper, which is titled Artificial Intelligence for Early Detection of lung cancer in GP's clinical notes.

So, yeah, it's great to see you both here today. And Martin, I'll come to you first.

I suppose we know that it's important to try and diagnose cancer early, but could you talk us through what's the potential for artificial intelligence here in terms of identifying cancer earlier based on patient records?

Speaker B

00:00:55.810 - 00:01:52.220

Yeah, that's a very interesting question because the potential kind of like goes hand in hand with the huge amount of interest in AI. And I think there are great opportunities. There are also great challenges.

But talking about the opportunities, especially in the context of the article that we wrote, is on the data side. So on the data side, the digitalization of electronic health records gives great opportunities.

A lot more is digitalized, and that means that we also, in our case, have access to free text, and that we, with the advent of the large language models, with also new developments in AI, we also have better ways of making use of those data. So those two combined creates a really interesting formula for big opportunities for AI in the general practice and healthcare in general.

Speaker A

00:01:52.300 - 00:02:05.960

And you mentioned access to free text records. So what GPs are typing into the record records?

But before we get into the study, can you just briefly describe what is natural language processing and how that can be used in free text records?

Speaker B

00:02:06.760 - 00:03:10.100

So we know that a lot of clinical risk scores, they work with features of patients, so their age and their gender or sex. And. But of course, a lot of information is also written up in unstructured way. And in our case that is text.

But we can also think of images and audio, and in that sense we have access to that data by different ways, which natural language processing is one of them. And it means that we give AI access to this text through, for example, advanced models like we now have, like ChatGPT users.

But that's only one extreme of the spectrum that we can talk about, because you could also imagine that we just simply look with keywords through the text, and then if certain keywords were mentioned, that you include that in the information that is available to your Docu to your, to your model.

Speaker A

00:03:10.260 - 00:03:18.820

And Hank, I don't know if you want to comment on just what we know already about clinical scoring systems for early diagnosis of cancer.

Speaker C

00:03:19.140 - 00:04:21.310

The problem with what we already know is that we know things because they have been coded in the past. If, if you look at the ways to access data, the only way to access data was by using codes.

And the big jump forward is made by using not only codes, but also text, because codes will always be replicating themselves.

By which I mean that a GP who likes to, to have to make notes of what he has been speaking about with patients, he cannot code all the things that he will write down.

So codes will always form a very exquisite extraction of the content of a consultation and will never present us with new information because codes only exist when the information was already there. Otherwise there will be no codes. Just so implicitly there is be a replication of what we know when we have to code our things.

Speaker A

00:04:21.899 - 00:04:49.139

Yeah, absolutely.

And I work with a colleague called Sarah Price who's done some research around coding and she's shown in her research that clinical coding can be biased depending on