From Big Ideas to Better Care: Live From PlatforMed 2026

Summary

---

Tomorrow’s Cure — “From Big Ideas to Better Care: Live From PlatforMed 2026”

Podcast: Tomorrow's Cure by Mayo Clinic
Date: August 12, 2026
Theme: Exploring how platform-based approaches in healthcare, powered by AI, data sharing, and global collaboration, are fundamentally changing patient care and accelerating innovation.

---

Episode Overview

This special live episode from the PlatforMed 2026 conference unveils how Mayo Clinic and its global partners are transforming healthcare from the inside out by creating a connected healthcare platform. Key voices—including Mayo Clinic's AI leadership, platform partners from organizations like Google Cloud and Aga Khan University, and clinical innovators—describe how AI, interoperability, and collaborative ecosystems are accelerating diagnostics, personalizing treatment, and democratizing access worldwide. The conversations spotlight real-world examples—AI for early cancer detection, global health data partnerships, and leapfrogging innovations in low-resource settings—showing the tangible impact for both clinicians and patients.

---

Key Discussion Points & Insights

1. The Promise and Challenges of Healthcare Platforms

[00:35–02:24, 05:31–06:48]

• Platform Approach: Mayo Clinic Platform connects data owners, innovators, and care providers worldwide to collectively accelerate medical innovation and improve outcomes.
• Goal: Impacting billions, not just thousands, moving beyond organizational boundaries to a global approach.
• Platform in Practice: Inspired by shared learning at Mayo’s core, now scaled up globally using technology.

“We have a tremendous amount of bottom up innovation happening at Mayo...but the complexity of AI meant we needed new governance to safely and quickly get solutions to the front lines.”
— Dr. Mickey Tripathy, 03:21

---

2. The Scale and Impact of AI at Mayo Clinic

[04:37–05:18, 32:35–34:52, 50:05–51:38]

• Current Pipeline:
  • 458 clinical AI solutions in development; 108 already deployed in clinical care.
• Types of AI Solutions:
  • Administrative simplification (scheduling, insurance, documentation).
  • Clinical breakthroughs (e.g., earlier detection of pancreatic cancer, automated summarization of health records).
  • Diagnostic innovation: AI models find cancer not visible to the human eye, with one example improving 5-year survival rates for pancreatic cancer from 3% to nearly 40% ([32:35]).
  • AI voice analysis may allow cardiac prediction from a patient’s speech ([50:05]).

“It brings tears to my eyes sometimes…I trust Mayo. Mayo’s not new to the trust game. We have been delivering trustworthy care for 150 years.”
— Dr. Mickey Tripathy, 34:11

---

3. Global Collaboration & Data Reciprocity

[09:09–10:24, 22:54–26:16, 40:49–44:30]

• Platform Analogy: Like Uber for healthcare knowledge—shared infrastructure, pooled data, global participation, benefiting both local Mayo patients and remote communities (09:09).
• Global Reach: Mayo Platform partners with hospitals and systems across North and South America, Europe, Asia, and Africa—each country retains and protects its data; questions can be asked of any location but data doesn’t cross borders ([24:18]).
• Data Reciprocity: Enables learning from every global patient, facilitating rare disease research and local adaptation of advances.
• Equitable Innovation: Global south partners like Aga Khan University use local data and adapt protocols, advancing population health with tools not optimized for high-income settings.

“Brazil, could you take all the data of the past and put it into a standard form and be part of a network? We can ask questions of it, but the data will never leave Brazil.”
— Dr. John Halamka, 24:18

“Insight only matters if it changes a decision. That decision is usually made when a community health worker is in front of somebody in a remote village.”
— Farhana Alarakia, 16:24

---

4. Industry-Ecosystem Partnerships: Google Cloud & Mayo

[10:40–14:07, 37:43–39:57]

• Google Cloud’s Role: Provides scalable, secure AI infrastructure (“the cake”), allowing Mayo to build specialized healthcare applications on top.
• Accelerating Innovation: Platform makes de-identified Mayo data accessible to vetted startups worldwide, spurring diverse solutions.
• Industry Impact: Over $170 billion invested this year in global AI infrastructure, enabling new ventures and quality control in innovation.

“A platform is as good as the participants on it. Best ideas come from the ecosystem, not just Google or Mayo Clinic. Patient deserves the best innovation, no matter where it comes from.”
— Ashima Gupta (Google Cloud), 11:45 / 38:20

---

5. Leapfrogging Healthcare in Low-Resource Settings

[14:22–19:34, 40:42–46:21]

• Aga Khan University’s Approach: Building health data repositories across Africa/Asia; using AI for population health in settings with few doctors and limited funding.
• Examples:
  • Predicting maternal hemorrhage from smartphone ultrasounds enables directing rural moms to better-equipped hospitals ([18:12]).
  • Kenya trains digital pathology models on local data to compensate for extreme pathologist shortages ([42:46]).
  • Rwanda leapfrogs with drone blood delivery instead of building a massive road network ([45:19]).

“We carry 25% of the global disease burden, yet only have 3% of the world’s healthcare workforce…We have to get ahead of disease.”
— Farhana Alarakia, 14:22

“We have one pathologist for every million people. In the US, it’s one for every 16,000 people.”
— Farhana Alarakia, 42:46

---

6. Data, Interoperability, and Patient-Level Impact

[25:35–26:35, 51:39–52:57]

• For the Patient:
  • Up-to-date, globally validated evidence guides care today, not outdated local protocols.
  • Personalization replaces crude diagnostic labels; the future is medicine tuned to the individual’s unique biology and context.
• Clinician’s Experience:
  • Improved workflows, less administrative burden.
  • AI as a force multiplier, not a replacement—amplifying human expertise and freeing up clinicians for higher-value work.

“Wouldn’t it, for a patient who needs care today, be the very best opportunity to look at every other patient in the world like you, and how they were treated yesterday?... The platform enables the patients of today to benefit from the experience of every other patient like them yesterday.”
— Dr. John Halamka, 26:00

---

7. The Future: Renaissance of Healthcare

[28:19–32:23, 47:26–49:11]

• Democratizing Knowledge:
  • AI can turn routine pathology slides into comprehensive molecular diagnoses, making advanced care accessible globally ([28:34]).
• Specialty-Agnostic Care:
  • We’re moving towards health delivery centered not on specialties, but on the patient journey and disease prevention.
• Hope for Disruption:
  • The real impact will be measured in stories of patient lives improved and systems truly transformed.

“It’s a renaissance…I think we will move to a place where we will become specialty agnostic…and focus on the patient and their journey.”
— Dr. Zadeh, 28:34

---

Notable Quotes & Memorable Moments

• On Mayo’s Global Ambition:

  “Mayo had a bold idea. Can we impact 4 billion people? That’s every single person on the planet with a digital connection.”
  — Dr. John Halamka, 05:50

• On Reverse Innovation:

  “I think we have an opportunity to leapfrog high income countries because we do not have the legacy platforms you have…That’s our big opportunity.”
  — Farhana Alarakia, 45:19

• On AI’s Human Impact:

  “Maybe the real promise of AI is not making healthcare less human, but giving us the tools to strengthen the most human side of care and care for one another.”
  — Lindsay Sievert (Host), 53:00

---

Select Timestamps for Key Segments

• The Mayo Clinic Platform Vision — [00:35–03:16], [05:34–06:46]
• AI Implementation at Mayo — [04:43–05:18], [32:35–34:52]
• Explanation of “Platform” in Healthcare — [09:09–10:24]
• Google Cloud on Platform Collaboration — [10:40–14:07], [37:43–39:57]
• Global Health Equity: Africa & Asia — [14:22–18:42], [40:42–46:21]
• Protecting Patient Data & Reciprocity — [24:18–26:16]
• Practical Patient Impact — [25:35–26:35], [32:35–34:52], [50:05–51:38]
• Future Vision & Hopes — [47:26–49:11], [51:39–52:57]

---

Tone & Style of the Conversation

The tone is collaborative, visionary, and deeply optimistic. There’s an undercurrent of humility—acknowledging uncertainty, the importance of local context, and the dangers of one-size-fits-all solutions—but also confidence in the power of combining cutting-edge technology with shared, global human knowledge. The experts interviewed, from engineers to clinicians, emphasize patient impact and ethical stewardship.

---

Summary Statement

From PlatforMed 2026, this episode of Tomorrow’s Cure makes plain that the future of medicine lies at the intersection of AI, global data collaboration, and local context—from recognizing pancreatic cancer years before it’s visible to empowering rural health workers in Africa. Mayo Clinic and its partners are building a platform not just for innovation’s sake, but for radically improved, personalized, and accessible care everywhere. The revolution is not just technical—it’s profoundly human.

Transcript

[Rachel Feldman]

Hey there. I'm Rachel Feldman and I host a podcast from Popular Science called the Weirdest Thing I Learned this Week. Every other week I circle up with guests like Bill Nye, Josh Gondelman, Mary Roach, and many more to prove that the lofty and noble pursuit of science can also be profoundly weird. From flying Ford Pintos to the world's most illegal cheese, the Weirdest Thing I Learned this Week is the ultimate source for all things interesting, informative and, and most importantly, frickin weird. Check out the Weirdest Thing I Learned this week. Wherever you get your podcast, come on over whenever you're ready to get weird.

[Dr. John Halamka]

So the role of Mayo Clinic Platform is to connect those who have data with those who are innovators, with those who need innovations. A three sided connection to build a community.

[Narrator / Host (Lindsay Sievert)]

Healthcare is always changing, but which advances are truly transforming care and which are still just big ideas? Platform Med is designed to make care more personalized, predictive and accessible, bringing together experts to examine how platform driven approaches are already reshaping patient care, accelerating research and driving innovation. This year, leaders in medicine, science, technology, AI and policy are exploring an even bigger question. What happens when healthcare starts working more like a connected platform, but learning from data clinicians and patients in real time? That's ahead on this episode of Tomorrow's cure, a podcast from Mayo Clinic that brings the future of medicine to the present. I'm Lindsay Sievert. We are coming to you from Platform Med in Scottsdale, Arizona. It's Mayo Clinic's annual conference on the future of healthcare. Throughout this episode you'll hear conversations with some of the conference's leading voices as they stop by our studio, fresh from the conference floor, to share where they believe healthcare is headed next. There's no one better to kick us off than Dr. Mickey Tripathy, Mayo Clinic's Chief Artificial Intelligence Implementation Officer. His entire job is focused on leading the safe and responsible adoption of AI across Mayo Clinic.

[Lindsay Sievert]

Well, thank you so much for joining us here on Tomorrow's cure. It's wonderful to have you.

[Dr. Mickey Tripathy]

Thanks, Lindsey.

[Lindsay Sievert]

So for people that don't know, tell us where you came from working for the federal government and now what you're doing here at Mayo Clinic.

[Dr. Mickey Tripathy]

Sure. Yeah. Just so just prior to this, I was in the Department of Health and Human Services. I was a Biden political appointee, an agency head. So I was the Assistant Secretary for Technology policy and the National Coordinator for Health Information technology. So we regulated nationwide data standards, all the electronic health records that are used in the provider setting. Later in the administration, I became the Chief AI officer as when ChatGPT exploded on the scene. So I was very excited in my next chapter here to be able to come to a place where I could get closer to how do all these technologies, starting with electronic health record systems and interoperability, which are things I'd worked on for decades almost, and then AI most recently, how do we bring all that to bear to improve patient outcomes and improve patient lives? And that's entirely what Mayo is about. Mayo and its history has always been focused on how do we improve patient outcomes in the best way that we possibly can.

[Lindsay Sievert]

So when you were recruited for this role, what was the main task ahead of you?

[Dr. Mickey Tripathy]

So the challenge was we have a tremendous amount of bottom up innovation happening at Mayo, which is kind of core to Mayo culture, I think going all the way back to the Mayo brothers. So that's been a core part of Mayo culture. But the technologies that are now available with sort of AI based technologies, no code solutions, all of those things has kind of set that on fire in a good way, not in a bad way. Meaning that now that culture of innovation is empowered by these tools that all of a sudden you can have front facing clinicians and staff being able to just take these tools off the shelf almost and start to develop really powerful technologies. Right. So we had a huge upsurge in the number of solutions, potential solutions that we wanted to be able to sort of bring to the forefront of clinical care. But the challenge that we had is that so many of them were coming up in a relatively short period of time. And the complexity of AI, we didn't really have a governance process that could sort of take account of this growing volume and the additional complexity of AI and say, okay, we can now meet that need and focus on getting these things over the line and into the front lines. We ultimately are accomplishing what we want to accomplish, which is how do we get these solutions into the hands of front facing clinicians as fast as we possibly can in a way that's going to improve patient outcomes?

[Lindsay Sievert]

How many AI models would you say, whether it's predictive or generative, is Mayo Clinic working on?

[Dr. Mickey Tripathy]

458 solutions that are in the pipeline, and these are clinical AI solutions that are in our pipeline. 108 of those, 458 have actually been implemented. So those are actually being used in clinical practice today to help improve patient lives. The other ones are somewhere on the journey and once they're ready for enterprise scaling, as we call it, which is say that's the, you know, the Product owner, the proponent who has the responsibility to do this is representing at that point that I've, you know, shaken this thing out and it's ready for primetime. I've tested it in a wide variety of ways and it's ready to be made available across Mayo.

[Narrator / Host (Lindsay Sievert)]

Dr. John Holamka jokingly calls himself the Forrest Gump of healthcare. By his own account, he's had a knack for being in the right place at the right time, from the dawn of personal computers to today's AI revolution.

[Dr. Zadeh]

So.

[Narrator / Host (Lindsay Sievert)]

So we asked him where healthcare goes from here.

[Dr. John Halamka]

I serve as president of Mayo Clinic Platform. I've spent the last 50 years, and I know that sounds weird because I'm just 64, but literally 50 years working at the intersection of healthcare technology and policy.

[Lindsay Sievert]

And what was the journey that brought you to Mayo Clinic Platform?

[Dr. John Halamka]

So I was the CIO of Harvard Medical School and a number of hospitals, Chief Information officer. One of the things you like to do is have an impact. And as a cio, of course you're building innovation and technology, but you're having an impact that may be in the order of thousands of patients. Mayo had a bold idea. Can we impact 4 billion people? That is every single person on the planet that has a digital connection today. I mean, even though there are 8 billion or so, not everybody has some kind of digital connection. And so a job description appeared and I had no notion of leaving Harvard, but nine of my colleagues said, you got to read this job description. It simply said, wanted physician, engineer with four decades of digital transformation experience, public policy and economics training to change healthcare globally. That sounded good.

[Lindsay Sievert]

This was calling you.

[Dr. John Halamka]

There you go.

[Lindsay Sievert]

And you've also been an advisor to the Bush and Obama administrations through your work at Harvard.

[Dr. John Halamka]

Well, the way that worked. I was called in 2004 and was asked, could you organize the data of the United States?

[Lindsay Sievert]

Oh, just a simple request.

[Dr. John Halamka]

It will take you eight hours a month. And so go run the committee responsible for all health data in the country. You'll bring together government, academia and industry and you will be in this context of what's called a federal advisory committee.

[Narrator / Host (Lindsay Sievert)]

Manish Goya likes to say you can't script a career. His own journey has taken him from venture capital and healthcare startups to helping lead Mayo Clinic Platform. We started by talking about why he believes the future of medicine depends on collaboration.

[Manish Goyal]

I'm the chief operating officer for Mayo Clinic Platform. My role is to handle a lot of the day to day operations, the growth of the platform, the delivery of the platform. I partner with Dr. John Halemka, who's my physician partner, and for those that don't know what Mayo Clinic platform is, it's really the embodiment of an extension of Mayo Clinic. So if we think of Mayo Clinic as delivering tertiary quaternary care at the core and then healthcare services around it, so like our lab services or Econsult, those are more services and transaction oriented platform sits on the outside of that, which is us enabling and partnering with academic medical centers, community hospitals to give them the tools that they need to do the things better. So if we can improve care quality at these hospitals, patients who don't come to Mayo Clinic get high quality care. And patients who do come to Mayo Clinic are now benefiting from all the learnings in these systems. So if you go back in time 160 years ago, the ethos of Mayo Clinic was shared learning. Our physicians and administrators go out into market, pick up all the things that are going on that we want to bring into our own practice and implement it. But we also teach the market how to do things differently. That shared learning is actually a platform concept. And so what we're doing is basically scaling it to the globe.

[Lindsay Sievert]

We hear the word platform used so much these days, and I think as a layperson it's hard to envision like what is a platform.

[Manish Goyal]

So 20 years ago, if you wanted to get a ride or go somewhere, you go on a bike, walk public transportation, or you'd call a cab. So when you think of what a cab company is, it's a set of cars that are owned by someone with a brand, yellow checkered cab, whatever it was, and the nose drivers are employed. So it's completely captive in a geography. And then there might be a different cab service in another geography about 15 years ago, along comes organizations like Uber and Lyft. They don't own any cabs, they don't employ anybody. They're global. Yet they're able to help you get from point A to point B. And so how did they do that? They created a shared infrastructure that anybody can now participate in. So platform is that, but we're doing it under a clinical knowledge paradigm. So now we've got large scale data that we've assembled and we're asking the world to contribute to that pool and benefit from that so that somebody who's coming to Mayo Clinic can benefit from the fact that we have partners all across the world that we have combined data sets for. But also if you happen to be in Nigeria or in North Dakota, you get the benefit of all of that shared experience.

[Narrator / Host (Lindsay Sievert)]

What happens when one of the world's largest technology companies collaborates with one of the world's leading healthcare organizations? That's exactly what Ashima Gupta thinks about every day at Google Cloud. We asked her what collaborations like this make possible for the best healthcare.

[Ashima Gupta]

I am the Global Director for Healthcare Strategy at Google Cloud. Been in the company 10 years in this role. I lead now our Genai strategy for our healthcare solutions and products, work with the industry at large and also serve as joint steering committee co chair for Dameo Clinic. Mayo Clinic is very special to me. It was 2019 when we first took the partnership. At that time the concept of a platform was just a concept. It was literally drawn on the paper on the back of the Napkin. And what Dr. Halamka and Maneesh have done, they build this platform from ground up and seeing that come to the idea, come to life and then a platform is as good as the participants on the platform. Every year when I come, I see more and more industry friends joining the platform. It's a global platform, as you know. So it's pretty rewarding for me to see how concept of having de identified patient data can make the ecosystem flourish in healthcare. We long believed that best ideas come from the ecosystem, not just from Google, not just from Mayo Clinic, not just from one or two companies. And that's the idea of the platform curation of the ecosystem. And I see that in practice what Vlameo Clinic has built is best ideas. Patient deserves the best innovations no matter where they come from. And that notion, that ethos is what platform Med represents. It's good to see ecosystem coming together, celebrating the success every year.

[Lindsay Sievert]

I would love to know how Google cloud partners with institutions like Mayo Clinic. Yeah, what is the relationship and how do you partner with healthcare institutions?

[Ashima Gupta]

If you look into the AI stack, it's multi layered stack all the way from. I'm good with food, so think of it like a five day cake.

[Dr. Zadeh]

Okay.

[Ashima Gupta]

Bottom is the infrastructure, data centers. We have data centers around the globe. We then build our own chips for AI. So think of AI computer, AI silicon. We build our own. So providing that because the AI needs that horsepower, the computing power, we build our own silicon. Then on top of that we build models. Models like Gemini that are tuned for the model. In doing that, that becomes the most vertically integrated stack. Then we build on top of that applications and solutions, the industry solutions like I was mentioning before and that stack all the way from data center to energy to chips to the model with

[Lindsay Sievert]

your cake analogy, the five layer Cake. Are you essentially sharing your cake with Mayo Clinic?

[Ashima Gupta]

They build their own recipes, but then we are providing the base layer, which is our infrastructure layer. So we imagine you don't need a kitchen. You can have one world class, if you can go in that analogy, you need a lot of equipment to be able to bake the cake at the industrial level that you're serving, or any innovation that you're serving globally. If you have the compute, you have the agentic technology. What applications can you build for your patients, for your employees, for your ecosystem partners? So Mayo Clinic brings that application layer. An application layer is where rubber makes their own. You can have all the computer infrastructure in the world and if you do not have meaningful applications on top, then that investment is means to an end. But the end is that meaningful applications. And that's the application layer. That's where Mayo Clinic is building.

[Narrator / Host (Lindsay Sievert)]

Some of the world's most exciting healthcare innovations aren't happening in Silicon Valley. They're happening in places like Kenya. Farhana Alarakia wants to make sure AI reaches the people who need it most. We caught up with her in between sessions at Platform Medicine.

[Farhana Alarakia]

I am the Chief Data Innovation Officer at the Aga Khan University and it's a not for profit university. We're actually in six countries, primarily low middle income countries, so Kenya, Tanzania, Uganda, Pakistan and Afghanistan. I am the Chief Data Innovation officer and what we do on my team is we build very large health repositories and then we apply AI to them to address population health challenges. And the reason that's very important for us is. I'll give you context strictly from an African perspective. In Africa, we carry 25% of the global disease burden, yet we only have 3% of the world's healthcare workforce, only 1% of global financing. From a healthcare perspective, that means the only way we're going to address our challenges, we have to get ahead. We can't have our people get sick. And the way to do that is we're making a big bet on data and AI.

[Lindsay Sievert]

This is your first Platform Med conference. It is, yeah. What kind of conversations are you having and what are you feeling energized about with colleagues and peers here?

[Rachel Feldman]

Yeah.

[Farhana Alarakia]

First of all, being a founding partner on the platform and then being at Platform Med, I'm excited by all the possibility.

[Narrator (NPR Up First)]

Right.

[Farhana Alarakia]

So like when I hear about Mayo and what they're able to do, whether it be with better predictions, early diagnosis. Right. The ability to then take those that knowledge, that intelligence, localize it for our context and into our systems like I said, big bet on data and AI. That's how we're going to address our population health challenges. I think the one part that is more exciting for me is I have met colleagues that are from other low middle income countries and we face the same challenges because for us it's not about the hospital.

[Lindsay Sievert]

Right.

[Farhana Alarakia]

The hospital is not where people come for care.

[Lindsay Sievert]

It's not the central.

[Farhana Alarakia]

It's absolutely not. It's in the community. And that care is not delivered by a doctor. It's delivered usually by a community health worker. And so how do we empower that? It's about taking this knowledge, but getting it to that point of impact. Usually the path to impact breaks down in one of three ways. One of those ways is insight needs to become action. Insight only matters is if it changes a decision. Right. And so that decision is usually made when that community health worker is in front of somebody in a remote village. So how can we empower them to either provide guidance or to provide contextual advice to that person so that they can have a better outcome? And so being able to talk about that with other colleagues that are facing the same challenges has been very exciting.

[Lindsay Sievert]

It's really interesting to think about the contrast of this large global platform affecting someone's decision in a remote village in a split second situation they might encounter. Right. That one person in that one moment. And when you say Africa carries weighs at 25% of the global disease burden, which diseases are most prevalent that you're looking for resources for?

[Farhana Alarakia]

We have certain issues that have been essentially taken care of in the global North. Let's talk about maternal mortality. Maternal mortality In Africa is 556 for every 100,000 live births. In the United States it's eight. In Canada it is four.

[Lindsay Sievert]

Is that deaths?

[Farhana Alarakia]

Yeah, four deaths for every 100,000 live births. So ours is 556. It's eight in the United States. In Canada it's four. In Norway last year they had to round up to zero.

[Lindsay Sievert]

Wow, that's vast.

[Farhana Alarakia]

There's a vast difference. Now you think about maternal mortality. A majority of the causes are treatable, right? Usually it's hemorrhaging, a previa.

[Lindsay Sievert]

Yeah.

[Farhana Alarakia]

We have blood, we have the treatment, we have the clinicians to be able to treat that. But usually there's something else. Maybe it's distance, the ability to get that blood, et cetera. And so what if you were able to predict an adverse event either for the mother or the child using a four month ultrasound taken through a remote device? We put it through a predictive model. It predicts whether there'll be an adverse event and so that mother is then directed to a higher care hospital where the blood isn't too far or they have a higher care practitioner instead of delivering in their remote village. That's how you address it.

[Podcast Promo Voice]

Busy healthcare professionals, this one's for you. Find Mayo Clinic talks on your favorite podcasting app or visit ce mayo.edu podcasts to learn more. Every week we share succinct, relevant and practical medical insights tailored for healthcare clinicians that you can immediately apply to your practice. Each episode covers common health issues seen in a primary care practice shared by Mayo Clinic experts.

[Narrator / Host (Lindsay Sievert)]

And here's more from Manish Goyal.

[Lindsay Sievert]

So you came to Mayo Clinic platform before the advent of AI, correct?

[Manish Goyal]

Yeah, this has been a journey for us. So seven years ago when we said, you know, how do we get large scale impact? So we were given one KPI per platform, which is how do we enable and partner with 50% of the healthcare providers globally? Pretty big, right? I fell out of my chair. And then when I got back up, I said, okay, I know now what we have to do. So you have to be relevant to other academic institutions that see themselves on par with us. Then you have to be relevant in parts of the world that there's no fixed infrastructure, that there's basically a village elder whose charge is to deliver care or community health workers. And so you have to be able to plug into the entire extreme ecosystem. So we said, how do you do that? Knowledge is the only transmittable thing. So if we can upskill everybody's knowledge, we all get better, whether it's our primary care docs in the US or that village elder. That's been a core part of our strategy is how do we get that innovation to happen at large scale across the world?

[Lindsay Sievert]

And so what are some of the measurable steps that you've taken in the seven years that you've been in your role as coo?

[Manish Goyal]

How do you AI enable this? So we organized all this data, but we were limited. We're limited by compute required to prosecute large scale data sets. AI is effectively a nice efficient tool to unlock knowledge from that data efficiently. Two years ago, if you were looking to put together an itinerary for a vacation with the family that had, you know, two teenagers with their own opinions and dietary restrictions and what activities you want it to do, that's a, you know, might be a Saturday afternoon or Sunday afternoon combined to put all that together. Now you can do this very efficiently in a chat. Sound Interface. So that's what AI does is it allows you to unlock knowledge efficiently with the task you're wanting to do. So we just applied it to medicine and so the impact then looks like better diagnosis, earlier intervention and, and ultimately better outcomes.

[Lindsay Sievert]

And that's done now with partners and data then globally.

[Manish Goyal]

Yeah. And so the reason we're called platform again is that we're not doing this alone. So we organized Mayo's data and we convinced seven other systems across the world to organize their data and share. Then we invited the world's innovators. So today we have over 180 companies across 14 or 15 countries that are developing solutions on the platform.

[Narrator / Host (Lindsay Sievert)]

And now Dr. John Holomka tells us more.

[Dr. John Halamka]

So the role of Mayo Clinic platform is to connect those who have data with those who are innovators, with those who need innovations, a three sided connection to build a community. Technology is just one piece. So we started in the United States, Mayo Clinic, Mercy, but then we moved to Canada. University Health Network or uhn, became the initial site in Canada and they're building a whole network out in Canada from their location, Toronto. Then we went to South America. Albert Einstein is the number one hospital in South America and again they served as the anchor, so to speak, for the whole of the South American region. And then we went to Southeast Asia and we went to South Korea and we went to Singapore and then Israel and then to India, Northern and southern, and now discussions throughout Europe as well as bringing in Mexico. So it's every month that goes by we bring a new geography into this peer to peer network for the benefit of patients.

[Lindsay Sievert]

That's why you've been to 21 countries this year, right? Yeah, let's say Albert Einstein system in South America, you say to them, we have this platform, we'd love to include your data. And in exchange you are able to leverage your organization. What's the sort of relationship that you broker?

[Dr. John Halamka]

Well, sure. So having done this for decades, what I could tell you is the following countries have regulatory restrictions on uses of data. So you don't say, hey Brazil, hand me your data. What we say is, Brazil, could you take all the data of the past and put it into a standard form and then be part of a network? We can ask questions of it, but the data will never leave Brazil. Now because you're part of the network, you can ask questions of any other country in the world. It's what we would call reciprocity. So wow, you mean I can look at every other country in the network and ask questions about Patient care and rare disease and drug discovery, the answer is yes, as long as they can ask it of you. And so this becomes an extraordinary bidirectional partnership where, as you heard, each participant benefits from each other.

[Lindsay Sievert]

So for then, the patient out there, I'm thinking of a friend who recently had a neck surgery at Mayo Clinic. What does this mean for them? So there's a platform. All this work, all these relationships forged behind the scenes from Brazil and beyond, how does that improve care at the patient level?

[Dr. John Halamka]

I went to medical school in the 1980s. Now, if you actually go back to the literature of the time and ask of the papers published in the 1980s, how many have been revised or redacted? Meaning what I learned was wrong. About 60%, really, of what I learned was wrong.

[Lindsay Sievert]

But that's the best at the time.

[Dr. John Halamka]

Well, it was the very best from the 1980s.

[Lindsay Sievert]

Right.

[Dr. John Halamka]

So wouldn't it, for a patient who needs care today, be the very best opportunity to look at every other patient in the world like you, and how they were treated yesterday? So I'm not gonna look at a textbook from 1980s. I'm gonna look at a patient who was cured yesterday.

[Dr. Zadeh]

Okay.

[Dr. John Halamka]

So in effect, a platform enables the patients of today to benefit from the experience of every other patient like them yesterday. Early diagnosis, treatment plans, what chemotherapy is going to work for you, what surgery is going to work? How do I avoid complications or infections?

[Manish Goyal]

That's one, two.

[Dr. John Halamka]

I mean, Mayo is a very innovative place, but it would be hubris to say we're the only innovative place. And so there are startup companies that have incredible innovation ideas, but no access to privacy protected, ethical, consented data. So if you take the energy of the entrepreneurs of the world and connect them in a privacy protected way to the data, they will innovate and spread their innovations. Sometimes there are organizations that will create AI, but here's a question for you. And again, this is completely made up.

[Lindsay Sievert]

Okay?

[Dr. John Halamka]

We're going to take the entire population of Minnesota and we're going to create an AI based on all their data, and we're going to use it in Mexico. Will it work? I don't know. Fewer Scandinavians in Mexico, probably.

[Lindsay Sievert]

Right?

[Dr. John Halamka]

So what you'd want is, is to say, well, there'll be innovators around the world and they'll develop these innovations in their home countries, but then they will send those innovations to other countries to refine them, prove them, validate them, qualify them as to how well they'll perform for another population. So there you go. So three ways. Evidence of the past for cures of today, helping us understand how innovation can be disseminated and how we can transcend geography and share those innovations in ways that will benefit local populations.

[Narrator / Host (Lindsay Sievert)]

Dr. Gallerizadeh is one of only a handful of women in the country leading a department of neurosurgery. And she's also helping shape the future of AI in medicine from the brain and beyond. Here's our conversation with Dr. Zadeh.

[Dr. Zadeh]

I'm the chair of department of Neurosurgery at Mayo Clinic in Rochester. And as Of January of 2026, I became the chief medical officer of Mayo Clinic Platform, which I'm very exc.

[Lindsay Sievert]

Congratulations.

[Dr. Zadeh]

Thank you.

[Lindsay Sievert]

We were talking. It's been quite a year or year and a half.

[Dr. Zadeh]

That's right. I moved from University of Toronto. I worked at Toronto Western Hospital, which is part of University Health Network, and I had a very big lab at Princess Margaret Cancer Research, which was all accumulation of years of me working to build an independent lab. But I wanted to move to a system that I could see different way of healthcare delivery. But more importantly, I was really excited about opportunities that Mayo Clinic is creating for what the future of healthcare looks like. And I wanted to be part of that. That was the main reason for me to move. So Mayo Clinic platform was one of them. The ability to have access to digitized slides, so digital pathology, being able to use AI and automation to really reimagine how we take care of patients. That sort of kind of contribution to being part of a bigger movement, which I see as almost like a renaissance of healthcare. There is integration of automation, digitization, AI that will really change how we think about disease. And I think we will move to a place where we will become specialty agnostic in large part and become very much focused on patient and their journey through their health care. And more importantly, be able to look at elements that contribute to disease. So we often focus on the person when they come to the hospital and touch our walls. We would like to see what are some of the factors that contribute to making you sick, making you ill, creating the disease, and so being able to prevent that, to intervene sooner and better understand the ability to predict. So the combination of all of that, that movement to me is a refresh, a restart. All of the innovation that comes with it is a renaissance. I think that's happening. It's an exciting period. To witness this and then more importantly to be part of it, to be able to make that movement happen is really exciting. I think it would. When we look back upon this era it would be something that we will remember with amazing memories of how we actually changed healthcare and transformed it. And I think you'll get to a place where you can take all of that and put it in the patient's hand and they would become the drivers. And so we, all of us, will be able to contribute to how healthcare will be shaped. One of the biggest priorities for me, and I think one of the greatest excitements of AI is that we can democratize knowledge. Currently, genomic studies can only be done in tertiary quaternary type institutions. It's a lot of money, it's a lot of expertise that's needed. And with introducing AI, we just had a publication last week where we demonstrate you can use a simple HNE slide and use AI algorithm to call out all of the molecular features on brain tumors. And so you can imagine molecular tests that were only possible at institutions that I work can now be available and accessible to everybody and in an affordable manner. So you would then be able to imagine how rapidly you can catalyze access to care. And more importantly, interpretation of knowledge could become more without borders and boundaries. And then as a result of it, the standards of care will then follow. That would change significantly because now we can actually apply the same standards that we have for Mayo Clinic more globally.

[Lindsay Sievert]

So that excitement is you're no longer just practicing in your lab in Canada or in Rochester, Minnesota, you're really thinking globally.

[Dr. Zadeh]

Correct.

[Narrator / Host (Lindsay Sievert)]

And now Dr. Mickey Tripathy tells us more.

[Dr. Mickey Tripathy]

We have an AI solution now looking for what's called visually occult pancreatic cancer, which is to say pancreatic cancer that is not visible to the human eye. So it's basically taking. One of the benefits that Mayo has is we have a tremendous amount of longitudinal data, so we have all of this data. So we can now take a solution and look back and say, well, of the patients who we know got pancreatic cancer, many of whom unfortunately passed away, we have their records going all the way back. So what if we took an AI and started looking at all the images of those patients from two years ago, three years ago, four years ago, and saying, and we know when we as Mayo Clinic diagnose that they have pancreatic cancer, what does the AI say for those patients? We go back and right now the early results are that it looks like that the AI can detect pancreatic cancer, which of course is a horrible form of cancer, because there are very few solutions, by the time you pick it up, can get it 18 months to three years before, when humans have been able to diagnose it. That's just tremendous. It's huge. And that's the difference. Statistically, that's the difference between right now, by the time you're diagnosed with something like that, your five year expectation for living is something like 3%. And if you can pick it up 18 months before that improves to 39%, almost 40%.

[Podcast Promo Voice]

Right.

[Dr. Mickey Tripathy]

It's tremendous. And again, it's the AI able to do something that a human can't do. So that's the, you know, that's the sort of the part that's improving or expanding the frontier of medical science.

[Lindsay Sievert]

Yeah, there's such a wow factor. Like we're at this watershed moment and I'm almost like stricken with disbelief that we're here. Right.

[Dr. Mickey Tripathy]

It brings tears to my eyes sometimes, I gotta say.

[Lindsay Sievert]

As patients, we're not able to peer behind the curtain and really understand this infrastructure. We're hoping it just leads to the best care possible for us.

[Dr. Mickey Tripathy]

Absolutely. And you shouldn't have to, frankly, you know, just because it's AI. I trust Mayo. Mayo's not new to the trust game. We have been delivering trustworthy care for 150 years. And so we believe very strongly that our patients should know that we haven't changed that dynamic, we haven't changed that trust that they should have in us. They should just know that we are adapting to whatever needs there are to make sure that they should have the same trust in us, regardless of the complexities of the technology that's underneath the covers.

[Lindsay Sievert]

Could you give some concrete models that have gone through the 108 that have been approved?

[Dr. Mickey Tripathy]

I like to think of them in two general categories. One is what are the models that just make work easier today? So it's kind of the work that we're all doing. But as we know, it's no surprise to anyone to say that healthcare has a lot of administrative processes. Let's say every patient knows this. Every time you try to do something, insurance and scheduling and all of those things, and prior authorization, all of those things. So there's a whole set of technologies which are about saying, well, these are all kind of in some ways human created bureaucratic things, administrative things that the healthcare system has a lot of. And what are the technologies that just make that easier to do? Right. It makes your day to day life easier. So better, faster, cheaper, humans can do it, but the technologies allow you to do it better. There's a whole second set of technologies though, which are about what are the things that these technologies can enable that Humans literally can't do that. It can take some kind of medical information, and I'll describe this in a second and get insights that the human senses can't get. Right. So you could have a human looking at something, a radiology image, a pathology image, whatever it is. And human senses don't allow them to see, oh, there's a cancer there. I see it. But you have AI technologies that can

[Lindsay Sievert]

actually look at that, enhance the image per se.

[Dr. Mickey Tripathy]

Yeah, it enhances the image. It's got the benefit of having looked at a million of them and saying that small little thing that your eye can barely see. This AI has been trained on a million of these images and can now say with a certain degree of confidence, that is a precursor to cancer. So we have this system called record time, which you can basically take all of these different modalities, upload it into record time, and record time will quickly go through, index and catalog all of those and say, here are all the discharge summaries. Here are all the X ray reports, Here are all the other imaging. Here are all the lab reports.

[Dr. Zadeh]

Right.

[Dr. Mickey Tripathy]

Put them into categories, and then it'll allow you to do keyword searches and it'll summarize some of those so that a clinician can now just go through and say, okay, I need to know whether they've had any imaging before. I need to know if they've had any of these MRIs before. Quickly pull that up and be able to get that assessment. It saves something like 11 to 15 minutes per patient. And we have hundreds and hundreds of patients. So it's a tremendous savings of staff time for just what's awful, very difficult work. But it also improves the quality of care.

[Narrator (NPR Up First)]

A lot of short daily news podcasts focus on just one story, but right now you probably need more on. Up first from NPR, we bring you three of the world's top headlines every day in under 15 minutes, because no one story can capture all that's happening in this big, crazy world of ours on any given morning. Listen now to the upverse podcast from npr.

[Narrator / Host (Lindsay Sievert)]

Here's Ashima Gupta from Google Cloud.

[Ashima Gupta]

This year alone, we will be spending over $170 billion in creating AI compute AI data centers across the globe.

[Lindsay Sievert]

I assume a record amount of investment.

[Ashima Gupta]

A record amount of investment. And we believe because of Google or Google Cloud's vertically integrated stack, we can be uniquely, very efficient in how we serve the responses from the AI, how we both are, the inference and the training costs. So those efficiencies are then passed over to customers like May or colleague. There's a lot of debate in the industry. AI is coming, AI is stacked, this compute is expensive and in that we give also the choice. For example, we have over 200 models. Of course Gemini model itself comes in different sizes. Not every use case needs the most robust model. If you look in the conference here and the Mayo Clinic platform, the type of startups that are getting built, the new ventures, the new innovations, it is being made possible because you have that early investment in building a de identified vetted Mayo Clinic platform where you getting different multiomics data. It's not just EHR information, pathology images, radiology images. If I'm a startup and I want to be able to build an application, I can join the Mayo Clinic platform and the value I will get is a de identified information. If that didn't exist, how else would I try that innovation or that application? To me that idea, that patient deserves the best innovation no matter where it is built. If you want to enable that idea, that means that innovation will come. We need to be inclusive and you need to harness that ecosystem. And that's what Mayo Clinic has done tremendously well. It's incredible success story. I have not seen any other platform with that scale and that level of data modalities in the industry. So it's truly one of its kind. This is a watershed moment for the industry.

[Narrator / Host (Lindsay Sievert)]

Here's Dr. John Halamka.

[Lindsay Sievert]

You became President of Platform in 2020 and that was really before ChatGPT came on scene in a big way, before AI really exploded for the everyday person. So I'm just wondering as you're developing this global platform and then AI is

[Dr. John Halamka]

here in 2022, it was November and this idea of ChatGPT was released to the world and suddenly everyone's imagination was sparked. They said that's really interesting. Can you start to train these frontier models, these generative AI products on the real world data of past patients. And so then Mayo began working on generative AI in earnest.

[Narrator / Host (Lindsay Sievert)]

Farhana Alarakia from the Aga Khan University in Kenya.

[Lindsay Sievert]

So Mayo Clinic platform approached you?

[Farhana Alarakia]

The Mayo Clinic platform came to us in terms of looking for a potential partnership. When I joined Aga Khan University, we built an electronic health record repository. That's this large population health repository that we built. And the reason that we were interested in a looking at the platform is data from the continent, not necessarily represented. When it comes to high income countries and Mayo with their reputation and their relationships with biotech and pharma, it's really important for us to have that representation 60 to 80% of healthcare protocols from high income countries from the global north do not work for us.

[Lindsay Sievert]

In what ways?

[Farhana Alarakia]

A good example is colorectal cancer. In high income countries, most screening does not begin until 50 or 55. 70% of colorectal cancer patients in Kenya present late stage. A third of them are diagnosed before the age of 40. I'm not saying that the model is wrong. It's wrong for us because we need that representation. So one, it's about having representation. Two, in general, Africa has not had a great relationship with the global North. HIV is probably the best example. And what we really loved is the partnership model in terms of not just the technology in that we own our data, we keep our data, we don't transfer it over. But it was the partnership in figuring out because for us it's not about the hospital setting, we need to get it out to the rural communities. And it's about discussing that. And that's why we were approached by many partners. But it is the only partner that we seriously considered.

[Lindsay Sievert]

So what does it mean when you said we're making a big bet on

[Farhana Alarakia]

data and AI in terms of the constraints that we have? We know we will never have the resources, whether it be people or money, to address population health challenges. We don't have the resources that high income countries have. Let's go back to the colorectal cancer example. We have one pathologist for every million people. In the United states, you have one pathologist for every 16,000 people. That's a big difference. I don't care what algorithm you give me, we're not getting ahead of that resource constraint. So what we did is digital pathology and AI pipeline trained on our data to allow us to, to stratify, to prioritize where we need to look. Because we can't send all those digitized slides to that one pathologist. It doesn't matter how fast they work, they're not going to be able to get through it. And so that is what I mean by we're making a bet on data and AI. It's the ability to see those patterns and to prioritize or stratify or direct or action our attention. If you think about what the Mayo Clinic platform will allow, we have data and it's using technology, it is using AI, it's building predictive algorithms. It's not necessarily using generative AI what the Mayo Clinic platform allows. Beyond this, it's also about us learning what's working, but then testing will it work for us? More often than not, it's not going to, but our ability to do that is now like this.

[Rachel Feldman]

Right.

[Farhana Alarakia]

It's not about me going, hey, you know, North America, I'm looking for a partner to compare, to understand, to learn from. We can go to the platform and we can say our rates of sepsis are this. What are your rates of sepsis? Let's run a comparison. What is one of your leading causes of sepsis? We learn from that. Here's our leading cause for sepsis. How did you address it? Because more often than not, it's been addressed in a high income country nine times out of 10, not in the same context. Right. And so therefore we need to localize that context for us. However, it accelerates the process for us.

[Lindsay Sievert]

So what could happen when you know, five, ten years from now, you're still in your last dance? What do you envision?

[Farhana Alarakia]

I think we have an opportunity to leapfrog high income countries because we do not have the legacy platforms that you have. I'll give you an example. So in Rwanda, let's talk about maternal mortality, right? Women in a remote clinic hemorrhaging during labor. A nurse puts in a request using a mobile device and within minutes a drone is delivering life saving blood. Rwanda didn't build a 20th century road network. Rwanda built a 21st century sky network. They leapfrogged, they didn't follow through traditional path. And I think that is our big opportunity.

[Dr. Zadeh]

Right?

[Farhana Alarakia]

That opportunity to leapfrog I think is substantial for us from a healthcare perspective and so accelerate five to 10 years. I think we're going to see reverse innovation where we teach the global north what to do.

[Narrator / Host (Lindsay Sievert)]

Ashima Gupta, if you and I are

[Ashima Gupta]

sitting here three to five years from now, let's say it's 2030, we will look back. So we are seeing two strategies unfold, which is present forward, what are we doing today, but future back. Where is the future of digital pathology, digital ideology? I believe the role of clinician would change, role of nurses would change. How we experience healthcare would change. How we experience technology around us will change. And every few decades technology comes like that. We saw that with the Internet. That's how Google was born, with the Internet and we all went online. I'm from that age group and we didn't grow up with the Internet. Then came mobile. Our perspective changed how we experience the world around us. But no mobile. Now there's a huge level of anxiety. If I leave my phone off your lap, my lap. Imagine that moment with AI, right?

[Lindsay Sievert]

How do we live without it?

[Ashima Gupta]

Some AI experiences we have imagined Some are yet to be imagined.

[Narrator / Host (Lindsay Sievert)]

Once more, Dr. John Halomka.

[Dr. John Halamka]

We have been talking to a number of low and middle income countries and asking questions like, well, how many pediatric oncologists do you have in your country? Zero. What if we could deploy an AI that would give the people of your country access to the world's expert pediatric oncologist. That would be revolutionary. So we've really, in 20, 25, 26, as we go to 27, moved from possibilities to impact and these solutions are getting deployed today and by next year we'll have many more stories at country scale of saving patient lives and improving care.

[Lindsay Sievert]

Navigation for everyone Dr. Galleries a day

[Dr. Zadeh]

it will take a long time for a robot or an AI driven robot to replace and maybe I'll be proven wrong in my lifetime is what I do as a surgeon. I don't think that intricacy, the focus, the ability to distinguish normal from abnormal tumor or other pathologies, to know what corridors to access the brain is something that in my lifetime a robot can do. I didn't know they'll be self driving cars 10 years ago, but maybe they will be self driving surgeons, I'm not sure. But more importantly, I could see a future where the years that we committed as a small lab that grew to be a sizable lab, 30, 35 people understanding the genomics, the molecular biology of brain tumors, could now be scaled so that it goes beyond our walls and allows scaling to other areas of healthcare and more importantly have global impact.

[Manish Goyal]

Maneesh Goyal Today we're in four continents. I think we'll be in six continents. I don't know if there's a health system in Antarctica, but let's just assume that we can find some partners over there. Yeah, so we'll have that. We expect to be covering close to 200 million lives. Today we're at 55 million. But the more important thing is I think we'll have closed the loop so we'll have been able to show how patients are actually benefiting. So we're starting to collect those patient stories of decisions made, leveraging what we're doing to really articulate the value. Because otherwise you get into a conversation around technology or business or operations, but it's not until you understand individual patient stories and societal impact that you know. So I'd say in two years we will have captured that story end to end.

[Narrator / Host (Lindsay Sievert)]

Here's Dr. Mickey Tripathy.

[Dr. Mickey Tripathy]

We're going to start to see just advanced technologies for diagnostics and therapeutics that no one even imagined. Like the pancreatic Cancer example I gave you. Increasingly, we're doing more work on voice, which is another example of. Right, like a whole new dimension where we've got a ton of research on this that's showing this now that you can detect certain types of cardiac conditions, for example. And Dr. Paul Friedman, who's the head of cardiology, is doing a ton of work in this space that you can tell through small changes in someone's voice against a baseline whether they might have certain kinds of cardiac conditions. And why is that? Because your vagus nerve goes right over your left ventricle, and then it goes up next to your vocal cords. Humans can't detect those changes, but if you have a baseline, the AI can detect the change and can say there may be this kind of cardiac condition. So let's now go look. What we're now starting to do is think of voice as a sample that you would give. Just like we draw blood and we might take a biopsy and we might take your height and weight and, oh, here in this iPhone, just read this script so that now we have a baseline of your voice. And so when you come back, we can run the voice through the AI and see if there have been any changes that might point out that, oh, there actually might be a cardiac issue. There is a huge upside here, and you as a patient need to understand the potential that this is going to have to improve your care and improve your care experience. And, you know, let's. Let's get on with that and do as much of that as we can.

[Lindsay Sievert]

And the challenge is for us to feel comfortable giving that voice sample just as much as we feel comfortable giving a blood sample.

[Dr. Mickey Tripathy]

Absolutely.

[Narrator / Host (Lindsay Sievert)]

Manish Goyal.

[Lindsay Sievert]

Our podcast, Tomorrow's Cure, is really sort of grounded in hope, and I'm wondering what you feel most hopeful about right now and what you're most excited about, especially here at Platform Medicine, our ability

[Manish Goyal]

to think differently about disease. It is no longer about the generalized concept of a disease, because if you think about disease, it's a label. It's a label that allows our brains to understand the internal processes that are too complex to explain. It's not too dissimilar to labeling somebody, you know as a sociopath. Right underneath, there's complexity that you can't explain. But if you could actually understand the complexity and then give us the right set of management processes that are unique to me as an individual, then we can get to better care. I know that's a complex thought, but it's almost like treating every individual you meet as a whole person versus a set of adjectives that you place on them. And that if we can do that, changes medicine completely. I think we're about to on the doorstep of rethinking our education processes, our delivery processes, our contracts for medicine that will be wholly disruptive and it's going to lead to better outcomes and frankly, lower cost.

[Lindsay Sievert]

It's exciting to open the door and walk through.

[Manish Goyal]

Exactly.

[Lindsay Sievert]

Manish Goyal, thank you so much.

[Manish Goyal]

Thank you.

[Narrator / Host (Lindsay Sievert)]

In this episode, we heard about AI spotting pancreatic cancer years before doctors, doctors can see it. Or the promise of how brain tumor diagnostics could someday be available almost anywhere in the world. So a patient in a remote village can benefit from the same knowledge as someone walking through the doors of Mayo Clinic. At the top of the episode, Dr. Mickey Tripathy told us Mayo Clinic alone has more than 450 AI solutions already in the pipeline. When Platform Med gathers again next year, there will be conversations about advanced we can barely imagine today, but maybe the real promise of AI is not making healthcare less human, but giving us the tools to strengthen the most human side of care and care for one another. Thanks for joining us on Tomorrow's cure. I'm Lindsay Sievert. We'll see you next time. Tomorrow's Cure is a production of Mayo Clinic with production help from the Podglomerate. Be sure to follow Tomorrow's Cure wherever you get your podcast and if you liked Today's episode, please like and subscribe. I'm Lindsay Siebert. Thank you so much for being with us.