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Three years after his first appearance, Butterfly Network CEO Joe DeVivo returns to catch Harry up on the roadmap. Butterfly's ultrasound-on-a-chip has evolved from a single handheld device into a foundational semiconductor platform that third parties can build on. The most dramatic example: Midjourney's full-body scanner, which rings 40 Butterfly chips around the body for whole-body tomographic imaging. Joe and Harry dig into what's real today versus what's coming, the physics of ultrasound, the path from demo to FDA clearance, and the business logic of "multiple shots on goal."   In this episode:   The chip as a platform: opening Butterfly's technology to third-party developers (the "Nvidia moment").   Midjourney's full-body scanner: tomography, 400 teraflops of data, and a roadmap toward a one-minute scan with the Apollo chip.   Real-world impact: 1,500 midwives trained in Kenya and South Africa, plus a new FDA-cleared gestational-age AI tool.   Partners beyond Midjourney: vascular robotics, fatty liver diagnosis, and brain-computer interfaces (including "silent speech").   Physics Q&A: why 360-degree imaging solves ultrasound's problems with air and bone.   The hospital shift: capturing "ghost" scans, Compass AI, and at-risk skilled-nursing deployments.   The 10-year vision: imaging that comes to the patient instead of the patient going to imaging.   Chapters:   (00:01) A roadmap from three years ago (00:31) Midjourney unveils a full-body scanner (01:31) Joe DeVivo returns (02:59) What shipped: the chip becomes foundational (04:52) Opening the platform to third parties (06:34) Kenya and South Africa: midwives and the FDA-cleared gestational-age app (11:11) FDA rigor and validating AI (14:52) Butterfly Garden, Butterfly Embedded, and the nine partners (16:53) Brain-computer interfaces and silent speech (22:00) The Midjourney deal: origin and reaction (24:03) Tomography explained (28:12) What 40 chips can see and the data challenge (29:33) The roadmap to a one-minute scan; the Apollo chip (32:43) AI looking for a physical carrier (34:31) Prototype to product: milestones to clearance (38:28) Wellness first, then regulatory clearances (39:03) The physics of air and bone (41:45) The investment thesis: multiple shots on goal (46:26) The business today: Compass AI and health-system deployments (49:54) Heart-failure monitoring and at-risk skilled nursing (53:49) The 10-year view: imaging comes to the patient (57:18) Why early detection matters (59:50) Closing   About the guest: Joe DeVivo is CEO of Butterfly Network and has spent 35 years in medical devices.   Also mentioned: Harry's book, The Invisible Interface: How AI Turns Intentions into Actions and Who Wins (foreword by Don Norman), available in print and ebook.

🎙️ In this episode, we discuss: 00:00 The Journey of an Innovator 06:27 The Birth of a Smartphone ECG 11:29 Overcoming Resistance in Digital Health 16:20 The Evolution of ECG Technology 23:43 The Importance of Early Detection in Cardiac Care 26:20 Innovations in 12-Lead ECG Technology 29:00 AI and Machine Learning in Cardiac Diagnostics 34:23 Remote Monitoring and Patient Empowerment 38:34 Navigating AI Diagnostics: Sensitivity vs Specificity 41:26 Consumer Wearables vs. Medical Devices 43:14 Future of AI in Cardiology and Personal Health Awareness

🎙️ In this episode, we discuss: 00:00 The Origin Story of Arrive Health 06:04 Rebranding and Evolving Mission 11:50 Real-Time Patient-Specific Drug Costs 18:08 Tackling Prior Authorization Challenges 22:56 Leveraging AI for Healthcare Efficiency 25:36 Understanding Scale and Impact 28:04 Collaboration with Payers and PBMs 30:27 Leveraging AI for Prior Authorization 32:43 Enhancing Access to Medications 36:27 Expanding Beyond Medications 38:19 Reframing Access to Care 40:51 Future Directions and Innovations 44:55 Wisdom for Innovators in Healthcare

🎙️ In this episode we discuss: 01:06 The founding story of Retina AI 03:56 Why ophthalmology is uniquely suited for AI solutions 06:27 Recent Series B funding and what it means for the company 08:31 How AI integrates into real-world clinical workflows 12:04 AI alerts and notifications for clinical decision support 16:36 Working with pharmaceutical companies on clinical trials 19:15 Predicting disease progression for untreated patients 25:42 The transition from traditional AI to newer approaches 29:28 Expanding beyond eye care with the Ikerian rebrand 32:06 Using the eye as a window to detect other diseases 37:44 How physicians have responded to AI diagnostic tools 41:09 Predictions for AI in healthcare and eye care by 2028

Chapters 00:00 Introduction and Company Updates 02:49 The Digital Transformation of Ultrasound Imaging 06:00 Advancements in Technology and Market Growth 09:03 AI Integration in Medical Imaging 14:50 Impact on Global Health and Humanitarian Efforts 20:55 Challenges in Mainstream Adoption of Handheld Ultrasound 29:43 Strategic Sales Approaches in Medical Devices 34:11 Finding Product-Market Fit 36:12 Simplicity in Medical Technology 39:26 Unexpected Use Cases and Market Adoption 45:24 Future Innovations in Handheld Ultrasound 51:58 The Importance of Patient Data Ownership

00:00 Introduction and Overview of Caristo Diagnostics 09:08 The Technology Behind Carry Heart 18:00 Clinical Implications and Risk Assessment 27:27 Actionable Steps for Patients 30:34 Optimizing Cardiovascular Drug Dosing 32:31 AI in Cardiovascular Medicine 33:50 Leveraging Historical Data for Risk Prediction 36:25 AI's Role in Molecular Pathway Analysis 39:03 GLP-1 and Cardiovascular Outcomes 41:56 Targeted Therapies in Cardiovascular Treatment 42:45 Building Trust in New Technologies 49:16 Regulatory Approvals and Future Prospects 54:15 Expanding Applications Beyond Cardiology 57:16 Looking Ahead: The Future of Caristo Diagnostics

In this episode of The Harry Glorikian Show, host Harry Glorikian welcomes back Jeff Elton, CEO of Concert AI, to discuss the latest advancements in AI-driven healthcare solutions. They reflect on the recent JP Morgan Healthcare Conference, highlighting the optimism surrounding AI's role in transforming drug development and oncology. Jeff shares insights into Concert AI's innovative data ecosystems, partnerships, and the introduction of Kera, an AI platform designed to enhance clinical decision-making. The conversation also explores the challenges and opportunities in the evolving landscape of healthcare technology, emphasizing the importance of collaboration and adaptability in the face of rapid change. Takeaways: The JP Morgan Healthcare Conference indicated a positive outlook for the industry. AI is becoming a central theme in healthcare discussions. Concert AI is developing a comprehensive data ecosystem for oncology. The introduction of agentic AI models is set to revolutionize data processing. Collaboration with NVIDIA is enhancing Concert AI's capabilities. Kera is a significant advancement in AI-driven healthcare solutions. The future of drug development will rely heavily on AI and data analytics. Healthcare organizations must adapt to the rapid pace of technological change. Building partnerships is crucial for addressing healthcare fragmentation. The integration of AI in clinical trials can significantly reduce timelines. Chapters   00:00 The Annual Healthcare Pilgrimage 03:19 Optimism in the Pharma Industry 06:07 The Rise of AI in Healthcare 10:03 Concert AI's Evolution and Innovations 15:10 2024: A Standout Year for Concert AI 18:55 Balancing Growth and Innovation 22:33 Scaling Across Therapeutic Areas 26:26 The Future of Collaborations in Healthcare 28:37 Integrating Immune Status and Data Collaboration 31:04 Introducing Kera AI: The Future of Data Management 35:24 Innovations in Clinical Trials and Data Solutions 40:16 Rethinking Drug Development: Digital Twins and AI 42:36 Navigating Market Shifts and Talent Challenges 54:02 The Future of Concert AI and Healthcare Solutions

Harry's guest this week is Raffi Krikorian, chief technology officer and managing director at Emerson Collective, the social change organization founded by Laurene Powell Jobs. Krikorian is the former vice president of engineering at Twitter (now X), where he was responsible for getting rid of the Fail Whale and making the company’s backend infrastructure more reliable; the former director of Uber's Advanced Technology Center in Pittsburgh, where he oversaw the launch of the world's first fleet of self-driving cars; and then the chief technology officer at the Democratic National Committee, where he helped rebuild the party's technology infrastructure after the Russian hacking debacle of 2016. At Emerson Collective, Krikorian built the technology organization, leads the development of data products, and works to upgrade the back offices of the non-profits Emerson works with. On top of all that, he recently launched a podcast called Technically Optimistic, where he’s taking a deep dive into the way AI is challenging us all to think differently about the future of work, education, policy, regulation, creativity, copyright, and many other areas. The show is a must-listen for anyone who cares about how we can build on AI to transform society for the better while minimizing the collateral damage. Harry talked with Krikorian about why he moved to Emerson Collective, why and how he started the podcast, and what he really thinks about what government should be doing to prepare for the waves of social change AI will bring. For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast  Please rate and review The Harry Glorikian Show on Apple Podcasts or Spotify!  Here's how to do that on Apple Podcasts: 1. Open the Podcasts app on your iPhone, iPad, or Mac.  2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode. 3. Scroll down to find the subhead titled "Ratings & Reviews." 4. Under one of the highlighted reviews, select "Write a Review." 5. Next, select a star rating at the top — you have the option of choosing between one and five stars.  6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long. 7. Once you've finished, select "Send" or "Save" in the top-right corner.  8. If you've never left a podcast review before, enter a nickname. Your nickname will be displayed next to any reviews you leave from here on out.  9. After selecting a nickname, tap OK. Your review may not be immediately visible. On Spotify, the process is similar. Open the Spotify app, navigate to The Harry Glorikian Show, tap the three dots, then tap "Rate Show." Thanks!

Generative AI is going to change how we do things across the entire economy, including the fields Harry covers on the show, namely healthcare delivery, drug discovery, and drug development. But we’re still just starting to figure out exactly how it’s going to change things. For example, AI is already speeding up the process of discovering new biological targets for drugs and designing molecules to hit those targets—but whether that will actually lead to better medicines, or create a new generation of AI-driven pharmaceutical companies, are still unanswered questions.  One thing that’s for sure is that generative AI isn’t magic. You can’t just  sprinkle it like pixie dust over an existing project or dataset and expect wonderful things to happen automatically. In fact, just to use the data you already have, you have to you may have to invest a lot in the new infrastructure and tools needed to train a generative model. And that’s the part of the puzzle Harry focuses on in today's interview with David Buniatyan. He’s the founder of a company called ActiveLoop, which is trying to address the need for infrastructure capable of handling large-scale data for AI applications. He has a background in neuroscience from Princeton University, where he was part of a team working on reconstructing neural connectivity in mouse brains using petabyte-scale imaging data. At ActiveLoop, David has led the development of Deep Lake, a database optimized for AI and deep learning models trained on equally large datasets. Deep Lake manages data in a tensor-native format, allowing for faster iterations when training generative models. David says the company’s goal is to take over the boring stuff. That means removing the burden of data management from scientists and engineers, so they can focus on the bigger questions—like making sure their models are training on the right data—and ultimately innovate faster. For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast  Please rate and review The Harry Glorikian Show on Apple Podcasts or Spotify!  Here's how to do that on Apple Podcasts: 1. Open the Podcasts app on your iPhone, iPad, or Mac.  2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode. 3. Scroll down to find the subhead titled "Ratings & Reviews." 4. Under one of the highlighted reviews, select "Write a Review." 5. Next, select a star rating at the top — you have the option of choosing between one and five stars.  6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long. 7. Once you've finished, select "Send" or "Save" in the top-right corner.  8. If you've never left a podcast review before, enter a nickname. Your nickname will be displayed next to any reviews you leave from here on out.  9. After selecting a nickname, tap OK. Your review may not be immediately visible. On Spotify, the process is similar. Open the Spotify app, navigate to The Harry Glorikian Show, tap the three dots, then tap "Rate Show." Thanks!

If you learned that radiologists looking at CT scans for the traditional signs of coronary artery disease catch only 20 percent of the people who actually have a high risk of a heart attack, and if you learned that there’s a new AI-based test that can catch subtle signs of inflammation in the other 80 percent of patients—well, you’d probably want to get that test yourself, right? Harry's guests this week, Frank Cheng and Keith Channon, are from a UK-based company that has developed just such a test. Cheng is the company's CEO, and Channon is co-founder and chief medical officer. And under their leadership, Caristo has introduced a test called CariHeart that applies machine learning to the data in a three-dimensional CT scan of the heart. It looks for otherwise invisible signs of inflammation in the fat tissue around the major coronary arteries, and then it predicts the chances that the patient will suffer a heart attack in the next eight years. Doctors can use that information to decide whether a patient needs to take a cholesterol-lowering drug like a statin or an anti-inflammatory drug like colchicine.  Caristo’s test is being used on an experimental basis in the UK, and it hasn’t yet been approved for use in the US. But it’s a leading example of the way AI, put together with fundamental advances in our understanding of human biology, is really beginning to change the practice of medicine. Cheng and Channon say Caristo’s test isn’t intended to put cardiologists or radiologists out of work—it’s designed to help them be more effective. And given that cardiovascular disease is the number one cause of death around the world, any technology that can help catch signs of coronary artery disease earlier could save a lot of lives. For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast  Please rate and review The Harry Glorikian Show on Apple Podcasts or Spotify!  Here's how to do that on Apple Podcasts: 1. Open the Podcasts app on your iPhone, iPad, or Mac.  2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode. 3. Scroll down to find the subhead titled "Ratings & Reviews." 4. Under one of the highlighted reviews, select "Write a Review." 5. Next, select a star rating at the top — you have the option of choosing between one and five stars.  6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long. 7. Once you've finished, select "Send" or "Save" in the top-right corner.  8. If you've never left a podcast review before, enter a nickname. Your nickname will be displayed next to any reviews you leave from here on out.  9. After selecting a nickname, tap OK. Your review may not be immediately visible. On Spotify, the process is similar. Open the Spotify app, navigate to The Harry Glorikian Show, tap the three dots, then tap "Rate Show." Thanks!

Investors and companies in the life science industry have been betting a lot of money over the last few years on a single idea: that computation will help us get a lot better at developing new drugs. But the word “computation” covers a pretty broad range of techniques. And the reason that there are dozens if not hundreds of computational drug discovery startups popping up is that everyone has their own hypothesis about what specific kind of computation is going to be the most powerful. For example, you might be convinced that the most important thing is to understand the physics of protein-protein interactions, at an atomic level. And so you would put your money into atomic-scale simulations that show how proteins fold or unfold to form different shapes under different conditions. Or you might think that it’s more important to model proteins at the molecular scale, to make predictions about whether and how a particular drug molecule might dock with a target protein. Or you might think that it’s smarter to try to model whole cells and see how different molecular pathways interact to affect different functions of the cell. Or you might not care about the details of physics- or chemistry-based models at all. In that case could just take a big generative AI model, similar to a large language model, and train it on huge amounts of unlabeled data about genes and proteins in diseases cells and healthy cells to see what kinds of predictions it comes up with. It’s too early to say which of these computational approaches—and which level or scale of focus—is going to be the most fruitful. But maybe you don’t have to choose. Maybe you can bet on all of these different ideas, all at once. Harry's guests this week are the CEO and CSO of a startup that’s taking an all-of-the-above approach. It’s called Deep Origin, and it was formed last year from the merger of two companies founded by theoretical chemist Garegin Papoian and software builder Michael Antonov.  Antonov helped to found the virtual reality hardware company Oculus. After Facebook acquired Oculus, he got curious about longevity and how software could help untangle the trillions of gene-protein interactions that mediate health and disease. He founded a company called Formic Labs to dig into that problem, and last year the company changed its name to Deep Origin. Papoian, meanwhile, is a former academic scientist who’s who also took the helm as CEO of his startup AI and who’s interested in how to use software to model molecular dynamics and quantum chemistry.  Recently Antonov and Papoian decided to join forces, and Biosim AI merged into Deep Origin. They say the company’s philosophy is that physics-based modeling by itself won’t be enough to build a powerful drug discovery engine. But neither will generative AI, which requires more training data than lab scientists will ever be able to provide. They think the only reasonable approach today is to combine the two, and use both physics and AI to try to get better at predicting which molecules could become effective drugs. Exactly how Antonov and Papoian came to their conclusion, and how that integration is playing out, was the main theme of this week's conversation. It’s important stuff, because if Deep Origin is right, then a lot of other more specialized biotech and techbio startups could be going down the wrong path.  For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast  Please rate and review The Harry Glorikian Show on Apple Podcasts or Spotify!  Here's how to do that on Apple Podcasts: 1. Open the Podcasts app on your iPhone, iPad, or Mac.  2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode. 3. Scroll down to find the subhead titled "Ratings & Reviews." 4. Under one of the highlighted reviews, select "Write a Review." 5. Next, select a star rating at the top — you have the option of choosing between one and five stars.  6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long. 7. Once you've finished, select "Send" or "Save" in the top-right corner.  8. If you've never left a podcast review before, enter a nickname. Your nickname will be displayed next to any reviews you leave from here on out.  9. After selecting a nickname, tap OK. Your review may not be immediately visible. On Spotify, the process is similar. Open the Spotify app, navigate to The Harry Glorikian Show, tap the three dots, then tap "Rate Show." Thanks!

If you look back at all the health-tech and drug development companies Harry has hosted on the show, an interesting pattern starts to emerge: a very large number of those companies have gone on to enormous growth and success in their markets. It could be that being on the podcast is like a catapult to success—or it could be that we're pretty good at finding companies that are already on a promising trajectory. Either way, there's no better example than Concert AI.  The company’s CEO, Jeff Elton, first spoke with Harry back in July of 2021. At that time, the company was already one of the leaders in gathering and analyzing broad collections of data about cancer patients involved in clinical trials for new treatments. Its specialty was, and is, going beyond the very specific endpoints measured in clinical trials and looking to electronic medical records, genome sequencing data, insurance claims data, and other sources in order to build a more comprehensive picture of cancer patients and their journeys through the healthcare system. That kind of data can be very useful to companies trying to track the performance of their drugs after they’ve reached the market, and to researchers planning new clinical trials.  And since that first conversation, the company has grown by leaps and bounds. It’s taken over management of more data sources, including the massive CancerLinq database formerly maintained by the American Society of Clinical Oncology. It’s struck up partnerships with some of the leading technology startups, research centers, and drug companies working to beat cancer. And it’s leaning hard into the new wave of deep-learning AI tools and their potential to help find patterns in vast amounts of data about patients. It’s probably safe to say that ConcertAI has gathered up more data about cancer patients than any other company on the planet. And investors have been rushing to pour money into the company, on the conviction that data is going to be the key to getting more and better cancer drugs to market. That’s certainly Jeff Elton's conviction too, as you’ll hear in today's interview. For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast  Please rate and review The Harry Glorikian Show on Apple Podcasts or Spotify!  Here's how to do that on Apple Podcasts: 1. Open the Podcasts app on your iPhone, iPad, or Mac.  2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode. 3. Scroll down to find the subhead titled "Ratings & Reviews." 4. Under one of the highlighted reviews, select "Write a Review." 5. Next, select a star rating at the top — you have the option of choosing between one and five stars.  6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long. 7. Once you've finished, select "Send" or "Save" in the top-right corner.  8. If you've never left a podcast review before, enter a nickname. Your nickname will be displayed next to any reviews you leave from here on out.  9. After selecting a nickname, tap OK. Your review may not be immediately visible. On Spotify, the process is similar. Open the Spotify app, navigate to The Harry Glorikian Show, tap the three dots, then tap "Rate Show." Thanks!

One of the most amazing successes in the battle against cancer over the last two decades has been the introduction of antibody drugs that harness the body’s own immune system to kill tumor cells. Finding those drugs may sound like a biology problem rather than a machine learning or a big-data problem. But actually, these days, it’s both. Harry's guest this week is Leonard Wossnig, who’s the chief technology officer for a UK company called LabGenius. The company uses a combination of synthetic biology, high-throughput assays, and machine learning to hunt for new drugs within a subclass of antibody medicines called T cell engagers that, loosely speaking, can grab tumor cells with one end and then grab tumor-killing T cells from the bloodstream with the other end. And Wossnig says the key to the whole thing is having the best data possible—meaning, data about their candidate T cell engagers and how specifically they bind to their targets in the lab assays. LabGenius has built an automated platform called EVA that runs experiment after experiment and uses active learning to zero in on T cell engagers with just the right ability to bind to their intended targets. One of the big takeaways from the interview is that companies that want to use AI to speed up drug discovery need the biggest, cleanest, and most consistent data sets possible. For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast  Please rate and review The Harry Glorikian Show on Apple Podcasts or Spotify!  Here's how to do that on Apple Podcasts: 1. Open the Podcasts app on your iPhone, iPad, or Mac.  2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode. 3. Scroll down to find the subhead titled "Ratings & Reviews." 4. Under one of the highlighted reviews, select "Write a Review." 5. Next, select a star rating at the top — you have the option of choosing between one and five stars.  6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long. 7. Once you've finished, select "Send" or "Save" in the top-right corner.  8. If you've never left a podcast review before, enter a nickname. Your nickname will be displayed next to any reviews you leave from here on out.  9. After selecting a nickname, tap OK. Your review may not be immediately visible. On Spotify, the process is similar. Open the Spotify app, navigate to The Harry Glorikian Show, tap the three dots, then tap "Rate Show." Thanks!

The combination of better data and more powerful computing is helping researchers reinvent the process of discovering new drugs. Within 5-10 years, we’ll likely see a huge wave of new medicines that were either discovered or designed using AI—drugs that will finally help us get control of our most stubborn health problems, from cancer to cardiovascular disease to obesity and metabolic disorders to neurodegenerative diseases. And the biotech startups that will do most to contribute are the ones that have both proprietary data, and original ways to use AI to sift through that data. Harry's guests this week are from a startup called Pangea Bio that’s working hard on both. As Pangea's co-founder and COO, John Boghossian, and its president of AI, Sona Chandra, explain, the company specializes in gathering data from the natural world, especially data about compounds manufactured inside the cells of plants and fungi. They narrow down the possibilities by working with indigenous cultures to find the plants or mushrooms that people have already been using for centuries in traditional medicine. They've also built three separate computational platforms that filter through all that data, to single out the small molecules that have the biggest effects in the human body, especially the central nervous system.  For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast  Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch: 1. Open the Podcasts app on your iPhone, iPad, or Mac.  2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode. 3. Scroll down to find the subhead titled "Ratings & Reviews." 4. Under one of the highlighted reviews, select "Write a Review." 5. Next, select a star rating at the top — you have the option of choosing between one and five stars.  6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long. 7. Once you've finished, select "Send" or "Save" in the top-right corner.  8. If you've never left a podcast review before, enter a nickname. Your nickname will be displayed next to any reviews you leave from here on out.  9. After selecting a nickname, tap OK. Your review may not be immediately visible.

There are about 30 trillion human cells in your body, but there are about 38 trillion bacterial cells, mostly hanging out in your large intestine. And that’s not even counting all the viruses, fungi, protists, and other microbial cells that live on your skin, in your bloodstream, and all around your body. So in effect, what you think of as you is not really you. You’re actually a walking colony of many different organisms. All of which cooperate peacefully, for the most part—unless the balance goes awry, and then you can get very sick, very fast. The microbiome has been getting more and more attention from researchers and doctors now that we’re starting to have the tools we need to identify and measure all those microbes and see what they’re up to. Harry's guest this week is serial healthcare and AI entrepreneur Leo Grady, whose company Jona is on a mission is to help patients and physicians keep up with the skyrocketing amount of scientific literature about the microbiome and try to translate it into real steps people can take to improve their health. If you’re a Jona customer, you start by sending in a fecal sample. Then the company uses a large-scale gene sequencing technique called shotgun metagenomics to get a profile of all the microbes in your GI tract. Since everyone’s microbiome contains a different mix of microbes, the next step is to use large language models to sift through the published science about the microbiome and find the studies that relate to the specific bugs in your microbiome. Then the company gives patients and their doctors a report that parses out whether their microbiome makeup might be contributing to their health problems, and whether there might be any health or nutritional interventions that would help. It’s all in the early stages. And right now Jona’s test is mostly available through concierge medical services, executive health clinics, and other offices that do a lot of cash-pay tests. But Grady thinks that over the long term the service has the potential to turn the microbiome from a former black box into something closer to what he calls an “organ of data"—meaning a part of the body that doctors can, in a sense, visualize and analyze in the same way we can use MRI and other forms of imaging to scan our other organs. For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast  Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch: 1. Open the Podcasts app on your iPhone, iPad, or Mac.  2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode. 3. Scroll down to find the subhead titled "Ratings & Reviews." 4. Under one of the highlighted reviews, select "Write a Review." 5. Next, select a star rating at the top — you have the option of choosing between one and five stars.  6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long. 7. Once you've finished, select "Send" or "Save" in the top-right corner.  8. If you've never left a podcast review before, enter a nickname. Your nickname will be displayed next to any reviews you leave from here on out.  9. After selecting a nickname, tap OK. Your review may not be immediately visible.