April 9, 2024
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.
59 min 27 sec
5.23.23
Large language models are already changing the business of search. But now they’re about to change the practice of medicine. Harry's guests, Vivek Natarajan and Shek Azizi, are both researchers on the Health AI team at Google, where they're pushing the boundaries of what large language models can achieve in specialized domains like health. This spring their team announced it would start rolling out a new large language model called Med-PaLM 2 that’s designed to answer medical questions with high accuracy. (The model got an 85 percent score on the U.S. Medical License Exam, the test all doctors have to take before they’re allowed to practice.) It's been clear for a while that consulting with an AI would eventually become an indispensable part of every medical journey—whether you’re a patient searching for information about your symptoms, or a doctor looking for an expert second opinion. And now that such a future is almost here, the work Vivek and Shek are doing at Google feels both exciting and a little bit scary.
60 min 54 sec
5.9.23
Harry's guests this week are Sri Kosaraju, the CEO of Inscripta, and Richard Fox, a former Inscripta scientist who just rejoined the company. In reabsorbing Infinome—the Inscripta spinout Fox described to Harry in a spring 2021 episode of the show—Inscripta is placing a big bet on biomanufacturing, the creation and fermentation of genetically customized microbes that can pump out medical, agricultural, and nutraceutical products, and more. Inscripta had previously focused on a benchtop "bio-foundry" machine called Onyx that that makes programmed edits to bacterial or yeast cells at thousands of different points in their genome in parallel. Now it's pivoting away from selling the machine and instead focusing on becoming a "power user" of its own technology—with the ultimate plan of marketing multiple biomanufactured products.
55 min 38 sec
4.25.23
Harry's guest Jen Nwankwo is the founder and CEO of the drug discovery company 1910 Genetics. The company focuses on finding the most promising new drug candidates for stubborn health problems—and it takes a refreshingly agnostic approach to everything else. 1910 doesn’t hunt for just small-molecule drugs or just protein therapies. It explores both. It doesn’t utilize just one form of neural networking or machine learning. It uses whatever model produces the best science for a given problem. It doesn’t hunt for drugs using just wet lab data or just computational simulations. It does both. It isn’t just assembling its own pipeline of drugs or just partnering with larger pharma companies. It’s working on both. At a time when AI and machine learning focused drug discovery companies are sprouting up faster than dandelions—each one touting some specific reason why its model is better than all the others—1910 Genetics is has a more inclusive approach to solving classic problems in pharmacology, and it’s one that should spread to other parts of the life science business.
49 min 42 sec
4.11.23
Tears are a signal of more than just our emotions. The liquid in tears comes from blood plasma, and contains a lot of the same proteins and other biomolecules that circulate in the bloodstream—including those released as a byproduct of the inflammation around tumors. Harry's guests Anna Daily and Omid Moghadam are from a startup called Namida Lab that’s the first company to market a lab test using tears to predict cancer risk. Specifically, Namida’s test assesses the short-term risk that a patient might have breast cancer, as a way of helping them decide how soon to go in for a mammogram. And beyond breast cancer, the company aims to build a whole business around risk assessment and diagnostics, using just the biomarkers in tears. Eventually it could be possible to collect a sample of your tears on a small strip of absorbent paper, send it in to Namida Lab, and find out whether you have colon cancer, pancreatic cancer, prostate cancer, or ovarian cancer. Namida’s big vision, as Moghadam and Daily tell it, is to use tear testing to make precision medicine and diagnostics more accessible and affordable, including to patients who might live far away from tertiary care centers.
42 min 34 sec
3.28.23
It may feel like generative AI technology suddenly burst onto the scene over the last year or two, with the appearance of text-to-image models like Dall-E and Stable Diffusion, or chatbots like ChatGPT that can churn out astonishingly convincing text thanks to the power of large language models. But in fact, the real work on generative AI has been happening in the background, in small increments, for many years. One demonstration of that comes from Insilico Medicine, where co-CEO Alex Zhavoronkov has been writing and talking since 2016 about the power of generative AI algorithms called GANs to help design new drugs. This February, in a milestone moment for the company, the FDA granted orphan drug designation to a small-molecule drug for idiopathic pulmonary fibrosis that Insilico discovered using its own GANs. Zhavoronkov joins Harry to talk about how Insilico got to this point, why he thinks the company will survive the shakeout happening in the biotech industry right now, and how its suite of generative algorithms and other technologies such as robotic wet labs could change the way the pharmaceutical industry operates.
89 min 37 sec
3.14.23
If we knew how to design small-molecule drugs to attach to binding pockets on any given RNA molecule to interrupt or modulate its functions, it could open up a whole new realm of medical treatments. The problem is, if all you know about an RNA molecule is its nucleotide sequence, it’s very hard to predict where those binding pockets might be and what kind of drug might fit into them. As a PhD student at Stanford, Raphael Townshend designed a deep learning model to tackle that problem. Called ARES, the model started with a proposed structure for an RNA molecule with a known nucleotide sequence, and predicted whether that structure would turn out to be correct compared to real-world data. It turned out to be stunningly accurate—and unlike the algorithms behind generative AI models like ChatGPT or DALL-E, it built up its skills based on a tiny data set consisting of just 18 examples of known RNA structures. Now Atomic AI is building on Townshend's original model to create an engine for discovering new small-molecule drugs that could potentially interrupt any disease where RNA is a player.
42 min 42 sec
2.28.23
The medical news publication STAT calls Will Flanary “the Internet’s funniest doctor.” The guests we bring on the show usually talk about how technology is changing healthcare, but Will and his wife Kristin are changing healthcare in a very different way—through comedy. A former standup comic who trained as an ophthalmologist and runs a successful ophthalmology practice in Oregon City, Oregon, Will is better known by his alter ego “Dr. Glaucomflecken.” His short videos have millions of views on YouTube and TikTok, and feature a cast of quirky characters, all played by Will himself, who lightly satirize medical culture and the idiosyncracies of the US healthcare system. And now Will and Kristin have a hybrid comedy and interview podcast called “Knock, Knock, Hi” where they bring on guests who share their own weird and hilarious medical stories.
49 min 48 sec
2.14.23
Within the last couple of years it’s become possible to sequence the entire genome of a newborn baby—all six billion base pairs of DNA—and diagnose potential genetic disorders in about 7 hours. That’s already happening in a handful of hospitals, with a focus on babies who are showing symptoms of rare genetic disorders. But within five years, says Harry's guest Dr. Stephen Kingsmore, it should be possible to extend this rapid whole-genome sequencing to every baby in every hospital, whether they’re showing symptoms or not. Kingsmore is president and CEO of the Institute for Genomic Medicine at Rady Children’s Hospital in San Diego, where he’s been leading an aggressive push to prove that rapid whole-genome sequencing and diagnosis can not only save the lives of newborns, but save the healthcare system a lot of money by making hospital stays shorter and therapies more directed.
43 min 13 sec
1.17.23
Harry's guest this week is Ryan Field, chief technology officer at a Los Angeles startup called Kernel. The company is developing a bicycle-helmet-shaped device that measures neural activity in the brain in real time. The first version, called Kernel Flow, contains more than 50 low-power lasers that beam light through the scalp and skull into the outermost layers of the brain. Hundreds of detectors built in the helmet collect the light that’s scattered back to measure oxygen levels in the brain’s blood supply, which is an indirect measure of neural activity. Field says the company doesn't have specific applications for the technology in mind, but he's betting that researchers and developers will come up with multiple ways to use Kernel Flow to help consumers gauge their state of mind or visualize how their brains are responding to different activities and therapies.
58 min 05 sec
1.3.23
Out of all the dozens of types of cancer that occur in humans, we habitually screen for only five: breast, cervical, colon, prostate, and lung. But what if there were a single test that could detect 50 types of cancer, based on a simple blood draw? That's exactly what's possible today, thanks to the Galleri test, introduced by Grail in 2021. The $949 test, which won breakthrough designation from the FDA in 2019, uses machine learning to assess the patterns of methyl groups—molecules that attach to chromosomes and control gene activity—in free-floating DNA shed by tumors. This week Harry interviews Grail's president, Dr. Josh Ofman, who says that if multi-cancer early detection tests like Galleri are eventually approved for population-level screening, it could help avert 100,000 deaths per year.
63 min 47 sec
12.20.22
Harry's guest this week, Carlos Ciller, started a company called RetinAI whose mission is to help eye doctors, eye surgeons, and scientists studying the eye manage and analyze the data from new kinds of eye imaging, including optical coherence tomography (OCT), fundus photography, and fluorescent angiography. At one level, RetinAI is just doing its part to cure a huge headache the show has revisited many times: the lack of standards and interoperability in the healthcare IT world. They want to make it possible to store and analyze digital images of the eye no matter what technology or device was used to capture it. But once that data is stored in a structured way, it’s possible to use machine learning and other forms of artificial intelligence to sort through image data and identify pathologies or double-check the judgments of human physicians. So RetinAI is developing algorithms that could make it easier to diagnose and treat common conditions like age-related macular degeneration—a form of damage to the retina that causes vision loss in almost 200 million people around the world. Ciller told me he started out his career as a telecom engineer and never thought he’d wind up running a 40-person company that works to help people with vision problems. But at a time when there’s so much new data available to diagnose disease rand identify the best treatments, journey’s like his—from the computer lab to the clinic—are becoming more and more common.
60 min 27 sec
12.6.22
This week January.ai co-founder and CEO Noosheen Hashemi returns to the show after her debut interview in July 2021. January makes a smartphone app that uses machine learning algorithm to learn how a diabetes or pre-diabetic patient's blood glucose levels respond to different foods. After collecting data from a wearable continuous glucose monitor, or CGM, for a few days, the app can start making predictions about a user's future blood glucose levels, even after they stop wearing a CGM. And that can help them make smarter decisions about what, when, or how much to eat, or how much they need to exercise after eating. Harry interviews Hashemi about the company's work to update the app, as well as a second company Hashemi co-founded, Eden's, to make supplements that promote gut microbiome health and—as a results—steadier blood glucose levels.
47 min 13 sec
11.22.22
About half a million babies are born every year through IVF. That number would probably be a lot higher if the procedure were cheaper and more accessible—but making that happen would mean transforming IVF from an artisanal craft into something more like a modern automated factory, with AI helping doctors and technicians make faster and better decisions at every step. And that’s exactly what Harry's guest Mylene Yao, the co-founder of Univfy, is doing. Univfy helps patients with two aspects of the IVF process. The first is using machine learning to provide patients with a more accurate assessment of the odds of success, before they decide whether to invest in one or more IVF cycles, which can cost up to $30,000 per cycle. The second is financing. Univfy works with a bank called Lightstream to provide up to $100,000 in financing for up to three rounds of IVF, with a large refund as part of the deal if the treatments don’t result in a baby. Harry talks with Dr. Yao about the prospects for far broader access to IVF, now that the field is finally adopting more ideas from the worlds of technology and finance.
57 min 19 sec
11.8.22
For the 100th episode of The Harry Glorikian Show, Harry welcomes Phil Febbo, chief medical officer at Illumina. The San Diego-based company is the leading maker of the high-speed gene sequencing machines that are at the core of the precision medicine revolution. The company has an 80 percent market share, which means that if you or your loved one has had any sequencing done for any reason, chances are your samples were sequenced on an Illumina machine. Gene sequencing is already a key part of both diagnostics and treatment decisions for many disease, but its use is only going to expand as the technology gets faster and cheaper. This fall, Illumina announced that it’s coming out a new gene sequencing machine called the NovaSeq X that can sequence a genome more than twice as fast as Illumina’s previous top-of-the-line machine, and at a lower cost. That’s bound to speed up progress all across the field of genetic medicine, drug discovery, and life science research. And that’s where Harry starts his interview with Febbo.
53 min 07 sec
10.25.22
In 1978, Louise Joy Brown was celebrated as the world's first "test tube baby," born as the result of in vitro fertilization (IVF). Today, Brown is 44 years old, and what was a technological triumph in 1978 is almost routine today, with half a million babies born every through IVF. But Harry's guest this week, gynecologist and investor David Sable, thinks IVF still isn’t nearly as reliable or accessible as it should be. From his studies of infertility services, he’s convinced that society is on the cusp of bringing down the cost and raising the success rate of IVF, so that it can finally become an affordable solution for millions more people every year who want to start or grow their families. And he thinks one of the keys to the next big wave of advances in IVF will be artificial intelligence.
64 min 35 sec
10.11.22
“LinkedIn meets ZoomInfo meets Zocdoc, but for doctors." That’s how H1 co-founder and CEO Ariel Katz describes the information service his company offers. It's a response to the fact that the healthcare is incredibly fragmented, with no central database or platform that everyone can use to share their professional profiles and get in touch with colleagues. (Physicians never adopted LinkedIn for this kind of networking because they just don’t switch jobs very often.) Without a central directory, patients can have a hard time find the right doctors, and doctors can have a hard time finding each other—say, when they might be searching for research collaborators. It’s an even bigger frustration for drug companies, who need to know which doctors can help them enroll the right patients for clinical trials. H1 is trying to solve all of those problems by building what Katz says will be the world’s largest graph database of people in healthcare. After participating in the 2020 batch of startups at the Silicon Valley incubator Y Combinator, H1 has rocketed forward, raising almost $200 million in venture capital. This week Ariel joins Harry to talk about how and why H1 has grown so quickly, and how better networking could accelerate drug development and help patients find the best doctors for them.
35 min 35 sec
9.27.22
If you walked into a typical life science lab, you might be surprised to see how much paper is still laying around. Many researchers still keep records of their experiments and studies in paper notebooks. In fact, along with doctor’s offices, biotech labs might be one of the last bastions of professional life that finally surrenders to digitization. But Harry's guest this week, Erwin Seinen, is helping to accelerate the shift. He’s the founder and CEO of a company called eLabNext, whose core product is a Web-based software platform called eLabJournal that includes tools for inventory and sample tracking, managing experimental protocols and procedures, and recording experimental results. Seinen explained to Harry how an electronic lab notebook can fit together with other lab tools, in an era where there’s just too much data to track everything on paper—and how companies can manage the transition to digital tools without sacrificing any of the spontaneity, curiosity, or creativity that good science is all about.
45 min 04 sec
9.13.22
Harry’s guest this week, Brian Pepin, says there haven’t really been any advances in the treatment of Parkinson’s Disease in a decade. The standard treatment is still the standard treatment—meaning various drugs to replace dopamine in the brain, since the loss of neurons that produce dopamine is one of the hallmarks of the disease. But there has been one important change during that decade. Thanks to new technologies, ranging from wearables like the Apple Watch to sophisticated deep brain implants from companies like Medtronic, we’re now able to gather a lot more data about what’s happening in the daily lives of patients with Parkinson’s, and how the disease is affecting their brain function and their physical movement. Which means there’s now the potential to make much smarter and more timely decisions about how to dose the drugs patients are taking, or whether they should think about joining a clinical trials. Gathering and analyzing that information and feeding it back to patients and their doctors in a user-friendly form is the mission of Rune Labs, where Pepin is CEO. He says we’re on the edge of a new era of “precision neurology,” where data gives doctors the power to predict the course of a disease and muster a meaningful clinical response. And he wants Rune Labs to be at the leading edge of that change.
51 min 33 sec
8.30.22
In most hospitals, the practice of radiology went digital years ago. Today you’ll rarely find a radiologist examining a broken bone or a fluid-filled lung on a sheet of old-fashioned X-ray film. But pathology isn’t as computerized. For a variety of cultural, technical, and regulatory reasons, many pathologists still prefer to look at tissue samples the old-fashioned way, on a slide under a microscope. Philadelpha-based Proscia is working to change that—and open up pathology to the power of remote work and automated image analysis—by building a cloud-based infrastructure for storing and sharing scanned pathology images. Harry’s guest today is Proscia CEO David West, who says there are still strong cultural barriers to the adoption of digital pathology, but “the community is realizing this can be really great for them and their discipline.” West says easier scanning, higher resolution, faster image delivery, and the ability to review images from anywhere and tap the power of artificial intelligence are powerful advantages driving adoption of Proscia’s platform.
56 min 42 sec
8.16.22
We use our smartphones to communicate, shop, navigate, watch videos, take pictures, share our lives on social media, track our exercise, and listen to music and podcasts. So why shouldn’t they also be the main interface to our healthcare experiences? Let’s talk about Vibrent mobile healthcare. P.J. Jain started Vibrent Health out in 2010 when he left behind a career in networking and telecommunications. The company had its breakout moment in 2015 when it won a contract from the National Institutes of Health build a mobile data-gathering infrastructure for a 10-year research program called All of Us, which is designed to gather medical data from more than a million people around the United States. NIH asked Vibrent to build a mobile app and an online portal that would become the communications backbone and the central data gathering repository for the whole project. And now that NIH is six or seven years into the project, it’s clear that in some ways the agency and the mobile interface Vibrent built for All of Us have leapfrogged over the rest of the US healthcare ecosystem. We’ll hear how in today’s episode.
58 min 34 sec
