# #APSA50: Artificial Intelligence — GCMD Library

This episode is the third in our #APSA50 series, where we teamed up with the Behind the Knife Podcast to cover the 50th Anniversary Meeting of the American Pediatric Surgical Association. In this episode, we interviewed the Dr. Michael Muelly, who gave the first of the meeting's PED (Pediatric Surgery, Education, and Disruption) talks on the topic of artificial intelligence in healthcare.

Dr. Muelly is a product manager at Google Cloud’s Healthcare & Lifesciences team and is a radiology faculty member at Stanford University. He has spent time researching machine learning, and has co-founded companies dedicated to bringing radiology to the cloud.

Stay tuned for more coverage of the 50th annual APSA meeting.

Type: podcast · 15 min · posted 2020-12-30 Canonical: https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445 ## Chapters - [0:00](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=0) Introduction and Background - [1:20](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=80) Current AI Applications in Healthcare - [4:34](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=274) Future Applications in Surgery and ICU - [7:41](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=461) Limitations of AI and Data Requirements - [10:47](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=647) Cloud Infrastructure and Ethical Considerations ## Statements - "Radiology has been thought of as one of the first fields where artificial intelligence will make an impact in healthcare" — Michael Muley (opinion) [1:39](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=99) - "At the Radiological Society of North America annual meeting, there are about 100 to 150 startups presenting in the machine learning showcase" — Michael Muley (epidemiological) [1:46](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=106) - "Yale has deployed AI for prioritization of head bleeds on head CTs, cutting down the time until a radiologist looks at studies with bleeds" — Michael Muley (clinical) [2:24](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=144) - "Most AI applications in radiology are currently in the operational realm, augmenting workflow rather than in the diagnostic sphere" — Michael Muley (clinical) [2:45](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=165) - "Evidence-based medicine has been discussed since the 70s, but most surgical training practices lack strong evidence behind them" — Michael Muley (opinion) [3:54](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=234) - "Laparoscopic surgery provides a natural opportunity for AI because it captures data digitally with video" — Michael Muley (clinical) [4:43](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=283) - "Surgical robots are not currently using AI tools primarily for regulatory reasons" — Michael Muley (clinical) [4:50](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=290) - "Trauma triaging is currently done based on very simple rules and is not very data driven" — Michael Muley (clinical) [5:28](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=328) - "Penn State rolled out a simple sepsis alert based on four criteria (elevated or low white count, temperature, heart rate) that did not work well because it went off too frequently" — Michael Muley (clinical) [6:29](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=389) - "Emory has sepsis alerts deployed into clinical workflow using deep learning" — Michael Muley (clinical) [7:06](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=426) - "Deep learning neural networks are very large and learn just by example, requiring many examples with good variety of the data space" — Michael Muley (clinical) [7:56](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=476) - "The type of acquisition device (GE versus Siemens) may affect what an AI algorithm learns in radiology" — Michael Muley (clinical) [8:11](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=491) - "There are approximately 7 to 8 billion people in the world, most without access to healthcare, and very few with medical conditions, making healthcare data availability very small" — Michael Muley (epidemiological) [8:31](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=511) - "Tesla has 100,000 to 200,000 cars collecting data daily for training self-driving capability" — Michael Muley (epidemiological) [9:11](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=551) - "Waymo has collected about 10 million miles of driving experience for self-driving car development" — Michael Muley (epidemiological) [9:26](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=566) - "AI models learn whatever associations are present in training data, including spurious correlations like rulers in melanoma images" — Michael Muley (clinical) [10:26](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=626) - "Cloud computing enables centralized data storage which makes it easier to create frameworks for data sharing between institutions for research" — Michael Muley (opinion) [11:33](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=693) - "A model trained on US data may not work anywhere else due to population differences" — Michael Muley (clinical) [12:26](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=746) - "If training data has racial or ethnic bias, the AI model will have that bias as well" — Michael Muley (clinical) [12:36](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=756) - "Training a machine learning model is a very small part of what needs to be done for clinical deployment" — Michael Muley (opinion) [13:00](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=780) - "How the FDA will decide what constitutes a safe medical device for AI applications remains an unsolved question" — Michael Muley (opinion) [13:12](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=792) - "AI models are much more consistent than humans even if not better, which is their great promise" — Michael Muley (opinion) [13:28](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=808) - "There is huge variance between how different radiologists read studies, sometimes incorrectly and sometimes due to difference of opinion" — Michael Muley (clinical) [13:38](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=818) - "Similar variance exists among ophthalmologists in their clinical interpretations" — Michael Muley (clinical) [13:53](https://library.globalcastmd.com/watch/apsa50-artificial-intelligence-1445?t=833) ## Transcript Welcome to the latest episode of Stay Current in Pediatric Surgery. Today's podcast was created and edited by Todd Ponsky, Alex Cassar, Ray Hanke, and me, Alexander Gibbons, in collaboration with the Behind the Knife team of Wu Do and Megan Akasha. This episode continues our coverage of the 50th ABSA meeting by interviewing the first speaker of the TED Talks series, Doctor Michael Muley of Google Cloud's Healthcare and Life Sciences team. He sat down with us to talk about artificial intelligence in medicine. Worried about being replaced by robot overlords. No need. Find out why in this episode. I'm Alexander Gibbons here at the 50th annual APSA meeting with Dr. Michael Muley, one of the guest speakers for this series of TED Talks. Dr. Muley's a product manager at Google's cloud's healthcare and Life Sciences team and is a radiology faculty member at Stanford University. He spent time researching machine learning and his co-founded companies dedicated to bringing radiology to the cloud. He's talking with us today about his personal views on artificial intelligence and how it pertains to healthcare. Doctor Muli, thank you for joining us. Hi, Alexandra, how are you? Doing well. So for our listeners who may not yet have seen how AI affects their day to day treatment of patients, what are some examples of how AI has already changed healthcare? Well, I think, uh, surgical specialties are, you know, somewhat naturally right now less affected by artificial intelligence applications, right? I think as a radiologist, right, and that is part of the reason why I actually went into radiology, um, there's a lot more data available and it's naturally digital. So radiology really has been thought of as one of the first fields where artificial intelligence will make an impact in healthcare, and it already is, right? So there's At our annual meeting, the Archaeological Society of North America annual meeting, there's about 100 to 150 startups presenting. In what they call the machine learning showcase, right, so all products, some of which you can buy right now, some of which are not yet available, somewhat promised, but you can actually see an impact on the field today with tools that are available from vendors and are deployed at certain healthcare systems. Do you have any examples that you know or have seen in action? So one thing that I know it's deployed at a couple of hospitals. Yale, for example, is prioritization for head bleeds on head CTs, right? So if there is a bleed in a head CT, those studies get prioritized over the rest, right? So basically cutting down the time until a radiologist looks at it. So most of them are really right now in the operational realm, right, really augmenting the workflow, not so much in the diagnostic sphere. Yeah, and I think that Sort of addresses a concern that some people have. There's the worry that, you know, robot overlords are going to be taking over and replacing doctors. What I'd like to point out is I'm not working at Google because I think my radiology job is going to go away, I think my radiology job is here to stay. I would love for the chest X-rays to go away. Even that I'm not sure about, really. It's. Yeah, how do you alleviate those concerns? I think it's education about what the capabilities of these methods are. Over the past 5 or so years, there certainly has been a significant hype, very strong worry in the radiology community a couple of years ago that radiologists. Going to get replaced, right? But if you actually learn about how these algorithms work and what the limitations are right now, that's much less of a worry, you know, but it's really, I think there is tremendous opportunity with artificial intelligence applications in healthcare to get to a, you know, truly evidence-based medical system. We've been talking about evidence-based medicine since the 70s, I think, right? And if you look at it, right, whatever you're taught during your surgical training is what you're going to do. Uh, is there evidence behind it? Most of the time not. There is, it's typically very, very weak, right? And so this opens up an avenue to collect these data sets, right, and gain new insights from those data sets that we previously were not able to do. And then you kind of alluded to how in the surgical specialties we're not really seeing much of this yet. Is there anything on the horizon that you can see or envision potentially being able to be applied to either surgery in general or specifically pediatric surgery? Yeah, I mean, I think pediatric surgery is even, you know, it's an even harder one compared to like general surgery or you know, some of the Let's say laparoscopic surgeries. Any time you do laparoscopic surgery, I think there is a natural opportunity? You capture data digitally with the video. There is obviously some surgical robots out there that are right now, primarily for regulatory reasons, not using any of these tools, once that hurdle is taken. will start to have artificial intelligence tools as part of that suite, right? I think there's a lot of areas where surgery will see this impact their work, but not directly in taking care of patients, right? So I think workflows, right, and prioritizing patients is something that's not just, you know, useful in radiology, but it's really useful in any specialty, especially surgery, right, where you, you, you know, Not really clear on how you prioritize patients, right? Uh, so let's say triaging, right, uh, in trauma, kind of done based on very, very simple rules and not very data driven, right? That's one good example of where, uh, I think there will be some impact in the. Next couple of years, right, if you talk about, you know, the actual operating room, you know, outside of the ancillary things, operational things, I think that that's gonna take, uh, a lot longer, if at all. Yeah, and I just, uh, listening to you talk right there, I can definitely see like AI being able to somehow. Incorporate all the slew of data that comes at us with uh the vital signs during a trauma or that's constantly monitored in the ICU and being able to kind of make sense of that huge amount of data that's coming our way and trying to, like you said, kind of triage and bring to our attention who we need to more specifically treat or treat sooner. And you know, actually ICU is a good example, right? Uh, so sepsis alert is something that people have been. Yeah, trying to do since the 70s. Uh, I remember when I was a surgical intern at Penn State, uh, we rolled out a simple sepsis alert based on four criteria of, uh, you know, elevated white count or low white count, temperature, heart rate. And we actually rolled it out into deployment at Penn State. Uh, it didn't work very well, right, because it was such a simple, dumb rule set. It just went off all the time, right? And obviously we had a process in place, right? The nurse pages, the intern. I kept getting many, many, many pages about that, right, of documents like, oh, it just went out for nothing. So we shut that down that experiment pretty quickly there, right? And today, you know, with the new types of artificial intelligence, particularly with deep learning, those alerts can be much, much more sophisticated, right? And so some places actually do have those deployed now, Emory being An example I'm aware of where they do have sepsis actually sepsis alerts actually deployed into the workflow nowadays, and you know at all how that's impacted their management of ICU patients or just that it is in place right now and they're still collecting that data? Yeah, I think they're still collecting data on actual outcomes. What are some of the limitations of AI at this time? And how do you think those limitations could potentially be overcome? Well, a big one is you need a lot of data, right? So this approach that we use today, deep learning, right, it's essentially very large neural networks. And they are really, really dumb, right? So they learn just by example. So you need to show it many, many examples of, you know, good variety of the data space you want to cover. Right. Uh, so, you know, different types of patients, different, uh, in radiology acquisition types, right? So whether you use a GE or a Siemens device actually may affect, uh, what the algorithm learns. So the limitation right now is really access to these very large data sets, especially in healthcare. So at Google, I like to tell our engineers who are used to getting billions of data points a day from search, and then they come and work on healthcare things. It's basically we have, you know, 7 to 8 billion people in the world right now, right? Most of them don't even have access to healthcare. You know, very few of them thankfully have anything wrong with them, right? The availability of data is very, very small. And then, Uh, you know, the more specific the disease gets, right, or your your patient population, right, let's take pediatric surgery, for example, the harder it gets, right? Collecting those big, big data sets with high quality, uh, labels in the end is really the big challenge right now. Yeah, because the AI, it's only going to be as good as the quality of the data that you're putting in. Right, so, um, self-driving cars are a good example. So Tesla, right, they have the opportunity to Collect all this data while those cars drive around. There's 100 200,000 cars driving around every day now collecting data for training the self-driving capability that they have promised. Similarly, VO, they have cars driving around, have collected about 10 million miles of driving experience now. It's very difficult to generate anything like that in healthcare, right? You're actually taking care of people. So first of all, it has to fit in the workflow when you collect the data. Then it's not like you can just simulate those things. How do you get large data sets is not just the availability of the data at all, right, but then there's technical reasons why you can't collect large data sets. There's organizational and regulatory reasons. So very, very challenging to actually create a data set to train these models. Even if they could perform all the tasks that a physician does. And then you mentioned like even type of machine being used can impact the data. I know another example is for image recognition for melanoma. They had a picture of a ruler for a lot of these, and so the AI ended up recognizing that if there's a ruler in the picture, then it's more likely to be malignant. Yeah, I mean, it's very, you know, those models are very, very dumb, right? They learn whatever you show them. They're really good at that, right? They are fantastic at learning those associations from images. If you give them enough data, right, but they learn whatever association is there that may be useful to learn that. That is a good example. On CTs, a good example we have is some uh marker that exists outside of the patient, right, that may tip the algorithm, uh, the model of that you're at one or another hospital where there's a different disease prevalence, things like that. And then with your work in bringing radiology to the cloud, you know, in a perfect world where we didn't have a lot of these regulatory issues, that would be a great way to generate these large data sets if everybody was able to upload it to the same kind of destination. Well, that is why I work on cloud right now. It's really, you know, I I think that this is for many, many reasons the way that data centers in general will have and healthcare specifically, which then opens up the opportunity of giving us access to larger data sets. Once the data is centralized, it's much easier to create the framework. That allows data sharing between institutions for research purposes. Just because the data is in the cloud doesn't mean that anybody has access to it outside of the rights holder. So you still have to solve these other issues. But once the data is in a format and in a place where you can easily solve the technical challenge, then it becomes much easier to solve the regulatory organizational challenge. I'm kind of in a similar vein. Um, what are some of the ethical concerns that you can potentially foresee as AI starts to play a larger role in medicine, and what standards do you think we can adopt now to kind of make sure that we are Preventing those issues from potentially getting larger. So I mean in healthcare, you're right, the ethical issues really resolve around bias of models. So a model that is trained on data in the US may not work anywhere else. Uh, it is very dependent on your patient population, right? So if you have any racial or ethnic bias in your data, the model is going to have that bias as well. And as you know, A lot of diseases have certain prevalences that differ across populations, so you have to take all that into account, really think about what the data is that you're using before you actually use it. I think training of a machine learning model is actually a very small part of what needs to be done. And then, I do think there's some questions about how do we regulate these things in healthcare. How is the FDA going to decide what is safe, what is a safe medical device, what is not? Those are currently still unsolved questions. I'm very excited to see these models actually make a real world impact again, I think. They are, you know, even if they're not better than humans, they're much, much more consistent. I think that's the great promise is consistency, or radiologists are a good example. Uh, an example where we have data, right, where there's there's huge variance between how different radiologists read studies, right. Uh, sometimes incorrectly, right? Uh, sometimes it's just a difference of opinion. Similarly, we have data for that with ophthalmologists, right, where there's also huge variance, and that really applies to, you know, anywhere where humans make decisions, right? There's there's always going to be variants, including pediatric surgery. So getting rid of that variance and actually providing consistent care according to what we know, I think is the great opportunity for these artificial intelligence applications. Well, thank you again very much for your time, Dr. Meli. I appreciate it and uh looking forward to hearing your talk at APSA. Thank you so much, Alan. This is Alexander Gibbons from Akron Children's Hospital, the contributing editor for this episode of Stay Current in Pediatric Surgery. We hope you enjoyed this interview with Doctor Muly about applications of the AI in medicine. His talk was one of the many great topics at this year's ABSA meeting. Please join us in the community section of the Stay Curtain Pediatric Surgery app to let us know what you think about our ABSA episodes and what you would like to hear from us in the future. The next episode will feature further coverage of the 50th ABSA meeting as we continue our collaboration with Behind the Knife. Thanks again for listening, and until then, remember, knowledge should be free. --- Not medical advice · citation policy: https://library.globalcastmd.com/ai