# AI for Interoperability in Health Care: Philips’s Carla Goulart Peron - **Show:** Me, Myself, and AI - **Published:** 1 Jun 2026 - **Source:** https://sloanreview.mit.edu/audio/ai-for-interoperability-in-health-care-philipss-carla-goulart-peron - **Transcript:** https://podcasts.opennash.com/shows/me-myself-and-ai/ai-for-interoperability-in-health-care-philipss-carla-goulart-peron-afb150/ - **Provider:** OpenNash Podcasts (machine-generated) ## In brief Philips chief medical officer Carla Goulart Peron argues that AI in health care should add to clinicians' capacity rather than replace them. Drawing on years in Brazil's stretched public system, she says AI can speed up scans, spread expertise to places that lack specialists, and free doctors to spend more time with patients. The central tension is trust. Health professionals are largely optimistic about AI, while many patients fear losing face-to-face time. Peron says closing that gap depends on better and more representative data, especially for women, and on interoperability, which she names as her top wish. Reimbursement rules, regulation and standardization stand in the way. ## Key insights 1. **AI scan automation cuts cardiac MRI setup dramatically** Peron says Philips's SmartHeart one-click tool plans cardiac MRI setup in about thirty seconds, versus fifteen minutes to thirty without it. That lets one machine and one technician handle more exams and reduces patient call-backs for missing views. 2. **AI is meant to add capacity, not replace** Peron says the gap in access to care is so large that technology will let health systems do more with the same resources. She notes patients can wait months for an MRI or CT scan, and that this happens in rich countries too. 3. **Patients and clinicians view AI very differently** A Philips Future Health Index found 79% of health professionals are optimistic about AI, while about half of patients worry it will reduce face-to-face time. Peron says clinicians worry about data and bias, while patients worry about the experience. 4. **Medical standards built on narrow data harm women** Peron says many guidelines were designed on studies that included mostly men, so women wait longer for diagnosis. Her example is a postpartum blood-loss threshold reportedly based on nine women in Germany, which later data suggested should be different in India. 5. **Interoperability is the AI capability she'd deploy first** Peron says letting clinicians see a patient's history across health systems would change outcomes most. The barriers she names are data standardization, reimbursement incentives that don't reward things like shorter hospital stays, and regulation built for an older era. ## Questions this episode answers ### Will AI replace radiologists? Peron says she believes AI is here to add, not take over, because the gap in access to care is huge and AI frees clinicians from low-value work like reviewing poorly captured images. She admits the conversation could look different in five years. ### What is Philips SmartHeart and why does it matter? It is an AI tool that, with one click, plans how a cardiac MRI should be set up for each patient, which Peron says takes about thirty seconds instead of fifteen to thirty minutes. This makes expensive machines more productive and lowers the need for highly trained technicians. ### Why do women get worse care for heart problems? Peron says women often show different symptoms, and many protocols were built from studies that included only men, so women wait longer for a diagnosis. Even small differences, such as the heart's position in the chest, can affect imaging and algorithms. ### What does interoperability mean in health care and why does Peron want it? Interoperability means different health systems can share and read a patient's data, so a clinician can see their history over time. Peron says this would change outcomes more than any other AI capability, because today care is limited to whatever system a patient is in. ### Why are patients wary of AI in health care? Peron says many patients fear AI will replace doctors and cut face-to-face time, partly because AI is a buzzword and people don't know what to expect. She argues it will instead free doctors for empathy and personal contact. ## Facts - **Carla Goulart Peron** [0:01:38]: "it's indeed a company that has 130 years and has been in many areas. But, uh, in the last few decades, Philips shift into healthcare fully." - **Carla Goulart Peron** [0:01:38]: "We start with the imaging, so the diagnostic area, x-ray, CT, MRI, ultrasound, and then we go into the interventional therapy into the cath labs." - **Carla Goulart Peron** [0:02:54]: "In the morning, I was working in the public sector in Brazil, where scarcity of healthcare workers, technology, information sometimes is very much present." - **Carla Goulart Peron** [0:02:54]: "But it's a universal healthcare, which means everybody has access to." - **Carla Goulart Peron** [0:04:54]: "I would need to transfer that patient sometimes to another facility, which means call an ambulance, be stuck in traffic, just to get the image capture" - **Carla Goulart Peron** [0:10:47]: "Smart heart is an AI-driven, one-click automation that plans all setups that drives how the cardiac imaging needs to be captured." - **Sam Ransbotham** [0:09:52]: "I think Philips just got FDA clearance for Smart Heart, which is a automated cardiac MR planning tool." - **Carla Goulart Peron** [0:11:20]: "for the operator that is actually doing multiple exams in different parts of the body with different indications, that can be from fifteen minutes to thirty seconds." - **Carla Goulart Peron** [0:08:57]: "Ensuring that the ideas that our engineers are developing in partnership with hospitals and physicians are going to meet the requirements of the regulation because we need to prove that it's safe and it's effective, right?" - **Sam Ransbotham** [0:16:13]: "I think part of your finding in that future health index was that 79% of the healthcare professionals are optimistic about AI" - **Sam Ransbotham** [0:16:13]: "like half of the patients are worried that it's going to reduce their face-to-face time." - **Carla Goulart Peron** [0:16:51]: "we are going to be launching a new version of the assessment a little later this year." - **Sam Ransbotham** [0:18:47]: "individuals who trust AI are twice as likely to use it regularly." - **Carla Goulart Peron** [0:21:21]: "Cardiac cases are the highest mortality rate present in female. Despite that, female have a much longer waiting time until to get diagnostic" - **Carla Goulart Peron** [0:21:21]: "most of the protocols that have been designed have been designed based on studies that included only male." - **Carla Goulart Peron** [0:22:02]: "the position of the heart is slightly different in the women's chest compared with the men's chest." - **Carla Goulart Peron** [0:24:02]: "there is a standard that defines that five milliliters of blood loss is normal." - **Carla Goulart Peron** [0:24:33]: "that standard was developed based on nine female in Germany and exploited and used in the entire planet." - **Carla Goulart Peron** [0:25:04]: "they quickly were able to iterate that with data points and kind of creating good correlation and establish that in India, that number should be 300 milliliters." - **Carla Goulart Peron** [0:24:02]: "I'm OBGYN. I have more than twenty years of clinical practice" - **Carla Goulart Peron** [0:27:46]: "We are still restricted by the reimbursement systems. What kind of code coverage do we have? What is the incentive?" - **Carla Goulart Peron** [0:28:20]: "The type of regulation that brought us here is not going to take us to the future, because the future is very different than the one we are playing today." - **Sam Ransbotham** [0:29:19]: "On the last episode of season thirteen, I'll be joined by Bernard Hampton, a corporate learning leader at Bank of America." ## Transcript **[0:00:02] Ad read:** How is one clinician thinking about applying AI to healthcare in additive ways that improve access to care clinician confidence and patient experience? Find out on today's episode. **[0:00:13] Carla Goulart Peron:** I'm doctor Carla Goulart Peron from Philips, and you are listening to me, myself, an AI. **[0:00:19] Sam Ransbotham:** Welcome to me, myself and AI, a podcast from MIT Sloan Management Review, exploring the future of artificial intelligence. I'm Sam Ransbotham, professor of analytics at Boston College. I've been researching data analytics and AI at MIT SMR since 2014, with research articles, annual industry reports, case studies, and now thirteen seasons of podcast episodes. In each episode, corporate leaders, cutting-edge researchers, and AI policymakers join us to break down what separates AI hype from AI success. Our guest today is doctor Carla Goulart Peron, chief medical officer at Philips. **[0:01:02] Sam Ransbotham:** Philips is a healthcare technology company behind imaging systems, patient monitoring, and a growing suite of AI-based clinical tools. What I find fascinating about Carl's perspective is that she started her career as a physician in Sao Paulo's public health system, where demand far outstrips resources. She's carried that lens into the C-suite. She's now leading medical strategy for a company that's betting heavily on AI to close gaps in care. Carla, welcome to the show. **[0:01:27] Carla Goulart Peron:** Thank you very much, Sam. **[0:01:29] Sam Ransbotham:** Many listeners might still associate Philips with more consumer electronics. But can you tell us about the company in terms of healthcare and the kinds of things you're doing? **[0:01:38] Carla Goulart Peron:** Yeah, of course, it's indeed a company that has 130 years and has been in many areas. But, uh, in the last few decades, Philips shift into healthcare fully. We start with the imaging, so the diagnostic area, x-ray, CT, MRI, ultrasound, and then we go into the interventional therapy into the cath labs. We also are very much present into the ICU and any area of the hospital where you are monitoring signals coming from those patients. **[0:02:11] Carla Goulart Peron:** Also heavily invested in monitoring things outside of the hospital as well. And then the last piece, but not less important, definitely AI that is supporting all those areas of care and then keep growing, you know, in a very hot topic right now. **[0:02:29] Sam Ransbotham:** Yeah, as you were listing these technologies, I was thinking, yeah, those are, you know, classic applications where AI has made huge advances with imaging and these sorts of other things. I mentioned earlier, you trained as a physician and you work there in both the public and the private sector, sometimes in the same day, I think, switching back and forth. So what did that experience teach you about where healthcare breaks down in a way that maybe technology can help? **[0:02:54] Carla Goulart Peron:** You are right. In the morning, I was working in the public sector in Brazil, where scarcity of healthcare workers, technology, information sometimes is very much present. So you need to work with the best of your capacity to offer care for those patients. But it's a universal healthcare, which means everybody has access to. So there is a benefit there too. And sometimes in the afternoon or in the night, I was working in the best of the best hospitals in the private sector with everything available. **[0:03:22] Carla Goulart Peron:** So I think that teaches you a lot of resilience personally as an individual, as a physician, but also you give you the the chance to try different things and learn from those experiences. But also makes so much clear to an individual like me, how much technology can actually build a bridge and help support more patients that are expecting to get access to health care overall, right? Whatever it is because it's expediting the way you are seeing those patients, because it's connecting the data points of information, or even allowing collaboration across the specialties that may not be present in the public sector. **[0:04:01] Sam Ransbotham:** So was there a specific moment that you just said this is something that technology could really help with or could help fix? Was there anything that sort of made you think technology might be the answer? **[0:04:11] Carla Goulart Peron:** Oh, many times, but I love sharing an example of ultrasound, where I was just coming out of residency, actually, and starting to see patients on my own. And ultrasound, it is one of the biggest diagnostic tools that we use in the OBGYN practice. But not always we had access to those machines in the hospital setup, and sometimes we had access to those machines, but we are not qualified to use them. And so technology came into reality today, and I'm very jealous about the people that are learning today their own clinical practice in a way that you have clear collaboration, and so you can really open the technology, open ultrasound machine, get access to an expert that can be anywhere in **[0:04:54] Carla Goulart Peron:** the planet, right? Let's just say in the same city, just to go and make it easier from the clinical practice perspective, to guide you to see what you are seeing, that same imaging help you to capture the right imaging and kind of expedite the technology. That in some other places, like the ones that were practicing in the public sector, I would need to transfer that patient sometimes to another facility, which means call an ambulance, be stuck in traffic, just to get the image capture and then patient back for you to be able to take a final diagnostic and initiate therapy. **[0:05:24] Carla Goulart Peron:** So, I mean, that was like wine and water, unfortunately, between those two words that I was leaving in. **[0:05:31] Sam Ransbotham:** Yeah, you mentioned the traffic. Not too long ago, I was in Sao Paulo, and that was a big thing. I remember there is just how long it took to get from one place to another. But actually, ultrasound are these even more expensive machines that can't move. But then there's also the other parts, the information that can move. And I think you were sort of making a distinction between those. You've got some aspects of what you're doing that seem to rely very heavily on sophisticated equipment. **[0:05:56] Sam Ransbotham:** At the other hand, you also have information which can flow without that bottleneck of Sao Paulo traffic. **[0:06:02] Carla Goulart Peron:** Yeah, I mean, I mentioned ultrasound, which is highly dependent of the imaging that you are actually seeing on time, right? So you use that imaging on time to kind of make the diagnostic. But when we think about CT, right, or MRI, they produce hundreds of imagings, right, that the radiologists can see from anywhere. And that is also facilitating drastically the way that we are actually reporting imaging. Also, you are highly dependent on the users that are actually placing the patient into the machine, making sure that the patient is well positioned, that is holding the breath if needed, or that is kind of moving accordingly. **[0:06:42] Carla Goulart Peron:** And now, with AI coming on board, you also can get that done very quickly, precisely, with not much support or training from the technical perspective. So that has been also a game changer, I would say the way we are leveraging technology, AI, automation to position those patients to make sure that the exam can be as fast as possible, but also how we are processing the imaging that is coming out of those big machines has been very different. **[0:07:12] Sam Ransbotham:** Okay, sorry, I think I was too simple. I was talking about the machine and then the result of the machine. But actually, you're bringing up an important point, which is there's also a a knowledge transfer and an information transfer about how to get the best image in the first place. And I think I I glossed over that. But you started bedside, but now you're running medical strategy for Philips. How does that inform what you think about the healthcare strategy and how that works? **[0:07:38] Sam Ransbotham:** Your deep background in actual doing medicine. **[0:07:42] Carla Goulart Peron:** You know, it's interesting because I think when you go into med school, most of the people that decide to take this pathway don't think about anything different than just seeing patients on a one-on-one base, right? At least when I start med school, that was the reality. I think today is very different. But in that journey of understanding the healthcare system, how companies that are developing drugs, medical device or other types of equipment work. I ended learning that there is a role to play in this industry side, in the corporate side, that can be as rewarding and as interesting as seeing patients on a one-on-one basis, probably in a much bigger scale. **[0:08:23] Carla Goulart Peron:** So I think I needed to convince myself that by moving from the bedside into the corporate, I was not changing my background as a physician or I was not leaving my professional behind. I was actually just applying that knowledge in a different way. So I'm fascinated by innovation. I'm fascinating to ensuring that whatever innovation we are investing on in the corporate environment can actually reach the patients that are going to benefit most. And that's really the biggest part of my job, right? **[0:08:57] Carla Goulart Peron:** Ensuring that the ideas that our engineers are developing in partnership with hospitals and physicians are going to meet the requirements of the regulation because we need to prove that it's safe and it's effective, right? But also that we have a good plan in how this technology can be incorporated by the healthcare system in the way that can actually reach the patients that are going to benefit most. It is different, it's very different, but it's fascinating because I keep learning every day because it's new technologies, new areas of care, right? **[0:09:28] Carla Goulart Peron:** New types of healthcare systems. If you think about a company like Philips that has a global presence, it's very different to think about commercializing something in Brazil than it is in the US. or in Europe, or in Africa, or in Asia. So you need to have that kind of globalized thought in mind when you are thinking about developing technology. **[0:09:52] Sam Ransbotham:** Well, let's talk about some of these specifics. I think Philips just got FDA clearance for Smart Heart, which is a automated cardiac MR planning tool. First, explain to people like me what that actually means, and then how does that actually change a radiologist's day? What's different? **[0:10:09] Carla Goulart Peron:** Yeah, so smart heart is a great example, right? You know, as you think about a MR machine, it is a technology that can capture imaging from your entire body, right? So, in order for you to do that, you need to have a technician that understand exactly why you are actually being requested as a patient to do that exam. So, in this case, physician, a cardiologist, most likely, wanna actually see how your heart is functioning. So imagine that a technician needs to know exactly how he or she should be positioning you into the MR table, uh, in which angle? **[0:10:47] Carla Goulart Peron:** If you are tall or short, if you are someone that is big or small, if it's a kid, or if it's a female or a male. There's so many different data points that a technician needs to understand in order to capture the right level of imaging with the right quality. That a radiologist can actually do a diagnostic out of it. Smart heart is an AI-driven, one-click automation that plans all setups that drives how the cardiac imaging needs to be captured. **[0:11:20] Carla Goulart Peron:** And this happens in thirty seconds. So that sounds like simple, but for the operator that is actually doing multiple exams in different parts of the body with different indications, that can be from fifteen minutes to thirty seconds. It makes the machine a lot more accessible, it makes the technician capable of doing a lot more exams, and also reduce the dependency of having someone that's highly trained. So the burden on the technician is really also something that today is a big issue out there. **[0:11:52] Sam Ransbotham:** I like the idea that we have a very expensive machine that we can increase the throughput for, like you said, fifteen minutes. That's four exams an hour, even without any, you know, setup and, you know, putting on the gown. But you talk about thirty seconds, and that suddenly I feel like, hey, we have an expensive machine that we can use a lot more. **[0:12:11] Carla Goulart Peron:** It is definitely right speed. There is a burden on healthcare providers and nurses, physicians overall, but also first time, right, right? So sometimes if you don't have something like this, you go through the exam, send the images to the radiologists, and they will say, Oh, you need to call this patient back because we are missing one or two views. And with something like that, this doesn't happen. **[0:12:34] Sam Ransbotham:** Yeah, nobody likes that. I mean, nobody wants to, you know, go back and forth and and Especially do these a bunch. **[0:12:39] Carla Goulart Peron:** in a machine that it's kind of like a you don't want to be there. It's small and somehow it may not be that comfortable for the patients overall. **[0:12:46] Sam Ransbotham:** But I think about radiology and radiology is one of the areas that I think a decade ago people were saying, oh gosh, we're never going to have radiologists again because the machines are going to do everything. And, you know, that narrative has really not played out at all. But I think it's a great example of just sort of in general how artificial intelligence might affect the future of work and what we do. But is there a risk that if this process works too well, hospitals are going to start thinking they need fewer radiologists? **[0:13:18] Sam Ransbotham:** How's that going to play out? Do you see? **[0:13:19] Carla Goulart Peron:** Yeah, I personally believe that AI is here to add, not to take over. Maybe this conversation will be very different in five years from now, because I think we Well, are learning that environment. **[0:13:30] Sam Ransbotham:** predicting is so tough in this world. **[0:13:33] Carla Goulart Peron:** But I think in the radiology space for now, radiologists are wasting their time in things that are not valuable at all, reviewing images that were not captured precisely, or doing reports, or reassessing a lot of normal images. Interesting enough, a few weeks ago, I was with one of the radiologists, magical societies, and they were talking about what if we could have AI defining all normal images, and then radiologists will be looking at only abnormal. And what's fascinating about AI and the potential? **[0:14:06] Carla Goulart Peron:** Someone in the audience raised their hand and said, Well, how are we going to get the radiologists trained in what is abnormal if they are not going to be seeing normal? So I think the answer for your question is to TBG in somehow, right? So what the future will look like to your point, but I don't see AI taking over. I think it's actually helping us to see more patients because there is a big gap out there. **[0:14:32] Carla Goulart Peron:** Also, to be dedicating our time to things that are unique that we can do as clinicians. **[0:14:38] Sam Ransbotham:** Yeah, one way I think about this is we would have a very different story if every possible patient in the world was completely satisfied with a perfect supply of radiology. Like, if everybody that needed this treatment was currently getting it, then I think we maybe could be talking about this replacement type of thing. But, I mean, you know far better than I do that that's just not the case. There's massive undersupply of these type technologies, and a lot of it is driven by the certain return on investment and the cost structure now, which I believe this can change. **[0:15:14] Sam Ransbotham:** Like, how do you see that aspect changing in terms of serving more? **[0:15:19] Carla Goulart Peron:** I personally believe that there is such a big gap, uh, out there in access to care, that as we incorporate technology, we'll be able to do more with the same, not with less, right? And because I was born and raised in Brazil and practice in Brazil, I feel very comfortable in going that pathway on low-income countries. In those areas, the gap is really huge. You can be waiting in line, uh, for months in order to have, as an example, an MR or CT scan done, right? **[0:15:48] Carla Goulart Peron:** Sometimes too late in the game that actually can change the the patient outcome. But we cannot fool ourselves that reality also exists in the US. and in Europe and in Asia. We have deserts, right? We have areas where people don't have any access to care. So I don't personally see a timeline where I would say, well, this will quickly fix that gap in a way that is going to be reduced. **[0:16:13] Sam Ransbotham:** Yeah, I I love the idea that what this leads to is better matching of need with supply. I've got a beef to raise with you. You had this future health index that you had put together. Normally, I like to skim these things before a call, but it was actually quite interesting. So it cost me a lot more time looking at it than I wanted. But I think part of your finding in that future health index was that 79% of the healthcare professionals are optimistic about AI, but like half of the patients are worried that it's going to reduce their face-to-face time. **[0:16:44] Sam Ransbotham:** How are we going to reconcile these two different perspectives from a market that has both parties are important? **[0:16:51] Carla Goulart Peron:** I think the perspective of the patients is very important in this one, right? I if the patients start to feel too uncomfortable and reject AI, it may become a challenge. I also think that this is slowly changing, and we are going to be launching a new version of the assessment a little later this year. But for me, the the ability to reconcile those two things from the the healthcare professional perspective is ensuring that we are validating. **[0:17:19] Carla Goulart Peron:** We're getting good access to data that is actually being used to train the machines, right? So physicians are on board, but really a little bit cautious about how much eventually bias do I get in the AI? Can I trust fully AI? How much do I need to review what's happening? So I think in the physician perspective or the healthcare worker's perspective, it's more towards data driven, while in the patient perspective, I think it's more about the experience. **[0:17:48] Carla Goulart Peron:** And I think AI became like a big buzzword, and so people don't know exactly what to expect. So there is this misperception that physicians are going to be substituted by machines, while the reality is the physician's time is actually being freed up to be actually dedicated more to the empathy piece, to the touch, uh, to that one-on-one eye-to-eye, which I think it's going to make a big difference. But at the second point, where I think it's very interesting, there are some studies already out there that show if you are talking to a real physician or to an AI version of that physician, sometimes the AI can learn how to be more empathetic than the physician. **[0:18:32] Carla Goulart Peron:** So I think this is going to be a journey of us as individuals, actually learning how to incorporate AI into our lives and trusting a little bit that help that I think we're going to start getting for future. **[0:18:47] Sam Ransbotham:** In our research, we found that, like, individuals who trust AI are twice as likely to use it regularly. That trust is an important part, but when we were studying it, we were sort of thinking in general, just use of AI. It strikes me that that's different about trusting Netflix's recommendation versus health care. But perhaps there is something transferable between the consumer levels of artificial intelligence and the greater understanding there and health care. Is there something unique about building trust in this clinical setting that is different than my Netflix example? **[0:19:23] Carla Goulart Peron:** I think people are more concerned about their health, right, than eventually getting a an advice of which movie I should be watching or which series. But one of the areas that I think AI is actually already doing for the patients is enabling, right? So I think even the name we gave to that individual that is in the center of everything we do as clinicians, patient, it's almost like you stay there, be patient and wait until somebody tells you what to do. **[0:19:50] Carla Goulart Peron:** Well, now, with, first of all, AI enabling interoperability, data points, you are giving more visibility to the overall health. And also, what are the options that those patients may have in front of them? And I think this is, in my view, is going to change drastically the way that we, physicians and patients, will be embracing health care for the future. Because I think, you know, I went to med school many years ago. Patients didn't have a say. **[0:20:23] Carla Goulart Peron:** It was really that's the protocol, and that's what we're going to do, and you just follow it. I think more and more we're starting to talk about precise medicine, where patients will be able to be offered one, two, three potential treatment pathways with pros and cons and the ability to choose. And I think AI may enable those patients to kind of make more informed decisions at least. **[0:20:46] Sam Ransbotham:** Actually, I really like the framing of the word patient, because I I feel like I often am not. We had Josh from CVS on, you know, one of the things we got into was, hey, I just would be, you know, forget all this AI stuff. I'd just be happy if I didn't have to put my name in over and over again and and wait for a long time. You can talk about all the cool AI stuff you want to, but uh, let's get some of those simple things done. **[0:21:06] Sam Ransbotham:** I think you're very focused on women's cardiac health. Maybe just orient us. What are some of the specific gaps in care for women? And where, like, how do you see AI perhaps connecting and either exacerbating or or helping with that? **[0:21:21] Carla Goulart Peron:** Yeah, I'm very passionate about the topic because I always say we need to remind people that, uh, we're not mini men. We are different as women, and we do have a heart and it needs to functions like any other heart, uh, in the man's body. So there is a big gap. Cardiac cases are the highest mortality rate present in female. Despite that, female have a much longer waiting time until to get diagnostic, because we experience symptoms different sometimes, because most of the protocols that have been designed have been designed based on studies that included only male. **[0:22:02] Carla Goulart Peron:** So there is a big gap out there that needs to be covered, and what technology definitely can help in kind of covering that gap, is ensuring that the nuances and the differences in the physiology and the type of response that female used to present is actually incorporated into the way we are designing the diagnostic tools. We talk about MR, right? So the position of the heart is slightly different in the women's chest compared with the men's chest. **[0:22:33] Carla Goulart Peron:** That's it's a very detailed minor thing, but it it can impact the way you're capturing your imaging. If you are capturing cardiac rhythm, for example, right? So the algorithms, uh, needs to understand that the female heart has a pattern that's slightly different from the male heart. And so I think AI will quickly get that information into those algorithms because of the speed, right, and be able to equalize that. **[0:23:03] Sam Ransbotham:** And that sort of presumes the fact that we've got these perhaps underrepresented or undertreated populations within our data sets. We had, uh, Ziad Obermeyer on the show a couple of seasons ago, and he was talking about how these algorithms can actually build up this equity. But it's depending on having that raw data to start with. What can we do to to try to get better sampling on those underrepresented populations? **[0:23:30] Carla Goulart Peron:** So the first thing is what we are doing right now is talk about it, right? So there is an opportunity for us to do better now. So you were spot on, right? So most of the drugs are developed based on a very limited number of female or even other diverse type of population, right? You name it, the same thing for medical devices, and most of the the protocols and guidelines that are created are also created based on trials that were developed a long time ago, which didn't necessarily include the right level of variety, right? **[0:24:02] Carla Goulart Peron:** That's the first point. The second point is that using technology and AI to capture that information pretty quickly and reiterate rather than starting from scratch. I've learned a big lesson two years ago when I attended the WEF for the first time, and we were talking about women's health, and I'm OBGYN. I have more than twenty years of clinical practice, and someone was telling me the story that when a woman gives birth, there is a standard that defines that five milliliters of blood loss is normal. **[0:24:33] Carla Goulart Peron:** So if you have that, you don't need to take any actions. And then I heard a question from the audience during that WEF discussion. How was that established? And I pause, cause I never asked her that question. I never had the curiosity. I just that wasn't the book. I just kind of assumed that a very good methodology was put in place, and that standard was developed based on nine female in Germany and exploited and used in the entire planet. **[0:25:04] Carla Goulart Peron:** Can you imagine how that translates to India or China, where they are much smaller bodies? So they quickly were able to iterate that with data points and kind of creating good correlation and establish that in India, that number should be 300 milliliters. So that makes a huge difference in how you're going to be treating your patients. And I think technology, that interconnectivity, that not only the fact that we're going into automation, right? But that now AI can analyze such a big data set so quickly can really improve the way we are practicing medicine. **[0:25:42] Sam Ransbotham:** Actually, that's an interesting example because it strikes me as a slightly different approach, which is in your example, you had to go to the World Economic Forum to have that question get raised. But I don't know. Somehow, I can also imagine a very simple job for agents would be, hey, go through all of our clinical practices in every area and find the root study for that and assess how that plays out. I would feel like your your nine person in Germany sample should rise pretty quickly to the top of that list. **[0:26:14] Sam Ransbotham:** That seems pretty exciting. **[0:26:16] Carla Goulart Peron:** Pretty exciting, exactly. It's fascinating what can be done. Yeah, **[0:26:20] Sam Ransbotham:** Let's look forward for a minute, though. You started in public health in Brazil. There's a lot of resource constraints there about technology. If you could pick one AI capability to deploy globally, that would make the biggest difference. You know, you get to choose right now, wish list. What would you push out to the world? What do you think is our greatest application of the use of artificial intelligence in health care that we could push globally? **[0:26:43] Carla Goulart Peron:** interoperability, that is going to change completely the way we practice medicine, because today we're very much closed or restricted to the healthcare system that you are operating. So the ability to kind of see the patients longitudinally without those barriers, I personally believe, is going to change outcomes significantly. If I need to pick one, that will be my choice. **[0:27:09] Sam Ransbotham:** That's not at all where I thought you'd go with that. That's pretty fascinating. It's so cross-cutting and it affects everything. All right, so I'll bite. What's the biggest barrier to that actually happening? **[0:27:20] Carla Goulart Peron:** Oh, that's a that's a big question. That's why it's a dream, you said. You can pick anything, don't get restricted. Uh, I think there are many, right? So the first one is making sure that we have access to good quality data, but also that we start thinking about that from the get go. If you don't have some level of standardization, it's very difficult to think about interoperability. I think that's the first piece, which is science. It's how we drive this for future. **[0:27:46] Carla Goulart Peron:** The second is how we think about incorporating this new era, well, how we think about incorporating reimbursement and access to technology into the discussion about AI. We are still restricted by the reimbursement systems. What kind of code coverage do we have? What is the incentive? We may have something that can actually pretty quickly take the patients out of the hospital, right? Reduce length of stay. But if that's not the incentive from the the healthcare system perspective, that's not going to happen. **[0:28:20] Carla Goulart Peron:** So, for me, that's another big thing that we need to think about. And the other piece, which I think it's it's how we need to partner, is regulation. Regulation will need to evolve with this new environment that we're getting into. The type of regulation that brought us here is not going to take us to the future, because the future is very different than the one we are playing today. **[0:28:44] Sam Ransbotham:** That seems really hard, too, because, yes, we built this regulatory system and we built these reimbursement processes and so many processes. We built off of the way things used to work. Thank you for joining us. I think what really comes through so clearly is that this just isn't abstract for you. This is something that you you know deeply, and you've seen what happens when care isn't accessible. And I like some of the ideas that you mentioned about how do we make the use of technology, not just AI, but technology in general, part of the solution and not just sort of a headline. **[0:29:14] Sam Ransbotham:** Thanks for joining us. **[0:29:16] Carla Goulart Peron:** My pleasure, Sam. Really appreciate the conversation. **[0:29:19] Sam Ransbotham:** Thanks for tuning in today. On the last episode of season thirteen, I'll be joined by Bernard Hampton, a corporate learning leader at Bank of America. Talk to you then. **[0:29:30] Ad read:** Thanks for listening to me, myself and AI. We believe, like you, that the conversation about AI implementation doesn't start and stop with this podcast. That's why we've created a group on LinkedIn specifically for listeners like you. It's called AI for Leaders. And if you join us, you can chat with show creators and hosts, ask your own questions, share your insights, and gain access to valuable resources about AI implementation from MITSMR and BCG. You can access it by visiting MITSMR dot com forward slash AI for leaders. **[0:30:01] Ad read:** We'll put that link in the show notes, and we hope to see you there.