AI Is Reading 15 Million X-Rays a Year With No Human in the Loop | Prashant Warier, Qure.ai
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Who is Prashant Warier and what is Qure.ai’s mission in AI‑driven diagnostics?
Is the doctor's office of the future going to be a bunch of these systems and the doctor kind of guides you? The AI gives the doctor a cheat sheet on what he should pay attention to. You go in and get a body scan, the doctor gets a report on what he should pay attention to. Is healthcare moving in that direction?
My personal belief is that primary care will be AI in the future, maybe five to ten years from now. We have to be more proactive about diagnostics and we'll see algorithms play a role in that. I think that's where definitely the world is headed where diagnostics will happen much earlier through the amount of data that we are generating and AI has some big role to play in that journey.
So uh let's start with with you introducing yourself to listeners.
Uh hi, my name is Prashant Warrior. Uh, I'm the co-founder and CEO of Cure. I've been uh building AI algorithms for the last 25 years. I did a bachelor's uh in technology out of uh in engineering out of India from one of the IITs, uhTelhi, and uh went to the uh US at Georgia Tech, did a PhD in operations research. My PhD was optimizing trucking networks for for the for US uh uh uh trucking organizations and um uh spent um uh I mean did that went on to work for SAP where I was uh basically doing price optimization for retail so price optimization markdown optimization demand forecasting a bunch of retail and consumer products problems um this was all in the US uh I came back to India uh about About 14 years ago, and uh set up an advertising technology startup, which was using AI to uh to basically collect consumer behavior from a lot of e-commerce sites in India, and then use that to target the right ads for customers.
And um then um uh that uh got an exit for that about 10 years ago and started Cure uh slightly less than 10 years ago, focused on AI. uh in the healthcare space and um b the yeah, we'll talk about the QR journey today.
Uh and and so talk about w what Cure AI was uh founded to do and how it has evolved.
So we when we started, uh our hypothesis was that um that image recognition algorithms, uh I mean, this there is AlexNet with that was one of these neural convolutional neural networks that was released in 2012, right? And uh our hypothesis was that can we sort of take some of those techniques apply to a lot of radiology images, billions of them, and would that enable us to then Identify abnormalities on an X-ray or a CD scan. And that was the hypothesis when we started. And of course, I mean we we did that. Spent a lot of time collecting data because getting access to anonymized, the identified customer data, patient data is not easy. And so spent almost the first year just working with hospital systems, especially.
We started out of India. So working with hospital systems in India. Getting uh de-identified patient data, basically radiology images in the corresponding reports, and uh created a large database, more than a billion and a half images now. And that went into training these algorithms. And uh the what we sort of focused on initially was detecting abnormalities on chest x-rays. Now, chest x-rays is the most common imaging modality, more than 1.3 billion chest x-rays taken around the world. Uh we focused on head CD scans. Head CD scans are one of those scans which require the fastest interpretation because you're looking for bleeds or stroke, and you want to be able to treat the patient quickly. So speed is important.
So high volume tasks, we looked at things where real-time processing is required and then sort of went on from there, built out uh solution portfolios for musculoskeletal x-rays, uh, chest CT. And a lot more. I mean, over the years, we've evolved into an early detection company. So we went beyond just radiology imaging. We said radiology imaging or detecting abnormalities on a radiology image is not enough. You have to actually be participate in the end-to-end diagnostic process from the time a patient comes in to all the way to the time they get diagnosed with the disease. How can you infuse AI?
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Chapters
7 chapters
1
Who is Prashant Warier and what is Qure.ai’s mission in AI‑driven diagnostics?
0:00–5:42
2
How did Prashant’s background in operations research and ad‑tech lead to building Qure.ai?
5:42–10:39
3
What was the original hypothesis for applying convolutional neural networks to radiology images?
10:39–20:40
4
How does the CREATE lung‑nodule malignancy risk score improve early cancer detection?
20:40–31:00
5
What is the fully autonomous TB‑screening workflow that reads 15 million X‑rays a year?
31:00–36:04
6
Which regulatory clearances (FDA, CE, etc.) support Qure.ai’s clinical credibility?
36:04–41:21
7
Why can patients not currently upload scans directly to Qure.ai and how might that change?
41:21–41:28
Speakers
1 identifiedMore from Eye On A.I.
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