Brainstorm AI London 2024: Boosting Breast Cancer Detection

Fortune

Presenter: Dr. Peter Kecskemethy, Co-founder and CEO, Kheiron Medical Technologies

Transcript

00:00 Thank you very much.

00:00 It's an absolute privilege to be here.

00:03 And I thought about that probably everyone here

00:08 has heard a lot about what AI could do,

00:12 what the risks could be, how it could be bad for humanity,

00:15 how it could be good for humanity.

00:17 But I'm bringing you slightly different things today.

00:20 And that is a very good news story,

00:23 how AI in practice is actually bringing impact right now,

00:27 today.

00:28 It's not a future, it's not a hypothesis.

00:30 It's actually what's happening now.

00:31 And I'm also going to bring you a secret, hopefully,

00:33 that you can take away from today,

00:36 and maybe a bit of motivation.

00:37 So let's get into that.

00:40 So my company, Chiron, has developed an AI called

00:45 Mia that is supporting doctors in early detection,

00:49 specifically breast cancer screening at the moment.

00:52 And that has got quite a bit of news coverage.

00:57 We were on the front page of the New York Times.

00:59 We were also named after our results

01:02 to be one of the seven biggest breakthroughs of 2023,

01:05 next to CRISPR and messenger RNA.

01:07 So in good company.

01:09 And there's a very good reason why that is the case.

01:13 One of the reasons is we are solving a very hard problem.

01:17 People may not know that breast cancer screening is very, very

01:19 complicated as a diagnostic task.

01:22 It's so complicated, some people say

01:24 it's like finding a snowflake in a snowstorm.

01:27 And actually, the practice of medicine

01:29 covers that, pretty much reflects it.

01:32 Most countries, outside the US, two doctors

01:36 are required to look at every single mammogram, so basically

01:38 every single image that is done for breast cancer screening.

01:42 And even with two doctors, we get at least 20%

01:45 of the cancers missed.

01:47 That means at least every fifth cancer gets missed.

01:50 That's like the best we can do across the globe.

01:52 And in some places, over 70% of the cancers get missed.

01:55 This is the best we are doing in screening right now

01:59 as humanity.

01:59 And you can see a couple of examples

02:02 of a hard case on the left and a hard case on the right.

02:05 On the left, a cancer was missed.

02:07 And on the right, a lot of the times

02:09 the doctors look at all the little tiny nodules

02:11 and they're thinking, OK, which one of these

02:13 is potentially a cancer, which one is not?

02:15 It's extremely hard.

02:16 Some people call it magic sometimes.

02:19 So what can AI do?

02:22 I'm going to talk you through this particular case.

02:26 This is an actual case where a woman went for breast cancer

02:30 screening regularly, so did the best she could do,

02:35 multiple years, and she died of breast cancer.

02:39 And she died of breast cancer because the cancer was not

02:41 picked at the last screening.

02:43 It was not picked up two years before.

02:45 And it was also not picked up four years before.

02:47 Every single time, missed by two doctors.

02:50 This is reality.

02:52 And what can the AI do?

02:54 The AI-- we ran the AI on many thousands, hundreds

02:58 of thousands of cases.

02:59 What the AI could do is find it at the last screening,

03:02 find it two years before, and find it

03:05 on the case for four years before.

03:06 So this could have been over four years earlier detection.

03:10 And we know that when breast cancer is detected early,

03:13 it's over 90% chance that a patient survives.

03:17 That could have been practically a life saved with AI.

03:20 So that's the impact what we can have on a single patient.

03:24 So let's see, what does it mean in reality?

03:27 So what you see here is how a doctor

03:30 looks at a particular case.

03:33 This is a medium complexity case.

03:35 It's not like a high complexity case.

03:37 You see a couple of nodules, but also it's relatively clean.

03:41 In this particular case, again, two doctors missed the cancer,

03:45 and the AI found the cancer.

03:47 This is not a very, very hard case.

03:49 This is a medium case, but the doctors

03:51 have to go through hundreds and hundreds of cases every day.

03:53 There's just not enough energy or attention for the doctors

03:56 to do their jobs well unless they get good tooling,

03:59 good help.

04:00 And I believe AI is one of the fundamental ways we can help.

04:06 In fact, that's exactly what we have shown in live use.

04:09 So what does it mean?

04:12 It means we can have extremely strong impact on a patient,

04:17 on the life of a single patient.

04:19 It also means, because we're talking about software,

04:23 this is automatically scalable.

04:24 You bring a piece of software, a box, to a hospital,

04:27 and you can help doctors start saving more lives.

04:30 That's practically what we're talking about.

04:32 And so roughly the impact that we

04:34 could have that Chiron is focusing on

04:36 is scaling it across the globe.

04:38 So we're estimating we can probably

04:40 help doctors save about 2,000 to 3,000 lives

04:43 every single year in the UK.

04:45 40,000 to 60,000 lives in the US, and hundreds of thousands

04:49 across the globe.

04:51 This is a piece of software that you bring across completely

04:54 scalably right now today.

04:56 And this is not a hypothesis.

04:57 This is based on actual evidence, actual results

05:02 that we have right now in multiple clinical trials

05:05 and multiple assessments across the globe.

05:10 So why is this big?

05:13 Why is it different from the past?

05:15 Actually, computer science and computer vision

05:17 has been used a lot, or at least there

05:20 were a lot of attempts to be used for breast cancer

05:23 screening, but not with too much success.

05:26 And there's a couple of reasons for that,

05:30 but I'm going to talk you through how we had

05:34 a slightly different approach.

05:37 We took two things extremely seriously.

05:39 And I think the importance here is

05:40 what can you learn from that?

05:41 How can you translate it to how you use AI

05:44 or how you develop AI?

05:46 And the two components of that is taking AI seriously,

05:50 that it's a very different type of technology

05:52 than humanity had before, and taking the domain seriously.

05:55 We're talking about a domain where any kind of decision

05:59 impacts a life, either way.

06:03 Either you can over-diagnose or you can potentially

06:06 miss a cancer.

06:07 So taking the AI seriously and taking the domain seriously

06:11 means that you need to collect a lot of data.

06:13 You need to have extreme level of validation.

06:16 I just need to make sure that when the software goes

06:19 to health care, that you're actually

06:21 impacting everyday the patients and the doctors.

06:24 It has a positive impact rather than a negative impact.

06:27 So if you want to know the details,

06:29 you can look it up.

06:31 I have a number of papers in Nature published,

06:34 and also a number of other publications.

06:37 But I'm just going to distill for you

06:39 some of the secret sauce that I believe we all

06:41 need to think about.

06:43 So hopefully, you can use that in your everyday practice.

06:48 And the secret here is that AI is not just a model.

06:52 You have a good model that you think is good,

06:55 is not going to necessarily-- or most likely--

06:58 not going to succeed in practice.

07:00 AI is a type of technology that we have never

07:02 had before as humanity.

07:04 Like, if you have a piece of hardware or software,

07:07 you know what it's used for.

07:08 You test it in a lab, and pretty much that's

07:10 how you're going to use it in everyday practice.

07:13 Your software, AI, specifically, it's

07:16 designed to be able to be intelligent and good on input

07:21 and in circumstances that it has never seen before.

07:24 This is a completely different-- that's

07:26 the difference between AI and other technologies.

07:29 So what does it need?

07:31 Of course, the models need to be good.

07:33 It needs to be good in the lab.

07:34 But also, it needs to be good wherever else you want to use

07:37 it.

07:38 So the algorithms have to be good.

07:39 OK, fine.

07:40 Whenever-- but we need to validate that, again,

07:44 both in the lab as well as in real world use.

07:47 However, we never know, because it's a black box,

07:49 whether it still generalizes.

07:50 Does it still work if I bring it to a specific hospital?

07:54 You need to validate that locally in deployment.

07:56 So that's the deployment stage.

07:58 And also, because it's a black box, it might work one day,

08:00 but it might not work next week, or might not

08:02 work a month or a year later.

08:04 You need to keep monitoring that it is still

08:06 providing the same performance.

08:08 So you need four things for AI to work in practice.

08:10 You need to get the algorithms right.

08:12 You need to validate it.

08:13 You need to make sure it works in practice,

08:17 both upon deployment as well as over time.

08:20 So please, please, if you are impacting lives

08:22 or you're having some kind of very, very important impact

08:26 with AI, do you make sure all of those work?

08:28 Because all of those are covered.

08:29 Otherwise, it's not going to be very good.

08:32 So after that, I'm actually going

08:34 to leave you with the words of one doctor and one patient who

08:39 can tell you about what it means for them

08:43 having used the AI in the UK.

08:46 [VIDEO PLAYBACK]

08:48 - I remember when I was a child, they

08:49 were saying they would eventually

08:50 find a cure for cancer, but it will take a long time.

08:54 And this is-- it's all just progression

08:56 down the right road.

08:57 - We read 5,000 mammograms a year.

09:03 There is a lot of pressure on the service.

09:06 I have starved shortages.

09:08 People are burnt out.

09:10 We have to make our decision first.

09:12 Then we are allowed to see the opinion of me.

09:15 So we press the little button.

09:16 It opens up and shows us yellow circles.

09:19 It says, can you have a second look here?

09:21 - I was quite surprised to hear back from them.

09:24 The penny didn't drop immediately

09:26 until they said they had found a very small amount of cancer.

09:31 And they said it wouldn't have been picked up by the human eye.

09:34 It was too small.

09:35 But the computer earmarked it, and that's how they found it.

09:40 - At the moment, we are using the AI in an evaluation process.

09:43 So we haven't integrated it fully into the system.

09:47 We have modeled that if we did have

09:49 the AI in our workflow, that would be a big difference

09:52 because it would make our turnaround time go from probably

09:54 14 days at the moment down to three days.

09:57 The technology helps move us forward.

10:00 This is the beginning.

10:01 Large scale, we can make a difference.

10:04 - It's a lifesaver, a life changer.

10:08 - So I will just leave you with that.

10:11 I think when we have enough evidence that AI works,

10:13 it's actually our moral obligation to use it.

10:16 Sometimes we need to be very careful.

10:18 Sometimes we need to go through all the steps to validate it.

10:21 But when we do, and it can have a very strong impact,

10:24 we need to actually use it.

10:25 And if you want to learn more, feel free to contact me.

10:28 [APPLAUSE]

10:29 [MUSIC PLAYING]

10:33 [BLANK_AUDIO]

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