We're now going to turn to a talk by Ashima Gupta. Ashima is the Director of Global Healthcare Strategy and Solutions at Google Cloud. In her current role at Google Cloud, she's accountable for driving healthcare strategy and solutions. She's instrumental in accelerating digital transformation in the healthcare industry through the use of cloud, artificial intelligence, APIs, and mobile solutions. She is a passionate visionary with expertise in driving architecture, digital technologies, big data analytics, and APIs. Welcome, Ashima. Hi, this is Ashima Gupta. I lead Healthcare Strategy and Solutions at Google Cloud. Thank you so much for inviting me to give this talk. First off, it's truly humbling to be here with all of you. Much respect towards the work you all are doing. As the world saw during COVID, science and research have taken the center stage and will continue to do so. Past 18 months have been difficult, and our mission to transform healthcare has never been more important. In the next 15 minutes or so, I'm going to cover a lot of ground, but the two key themes that I want you to take away are power of collaboration and how Google thinks about helping people and organizations with helpful tools. So let me begin by sharing a non-stop. Google's mission is to help organize both information and make it accessible and useful. And our mission in healthcare is a reflection of that. In that, we want to help organizations, researchers, scientific community around the globe to help them organize their information and make it accessible and useful. Core to our mission is Google started as a consumer company. I'm sure you use one or some products from Google, Google Search, YouTube, Google Maps. And for years, we have been investing and scaling our infrastructure, our data tooling to meet our own business and need to scale our products to billions of users. In fact, we have nine products with a billion users each. And while we were building this product, there's a core of AI and machine learning that came out of necessity. So when you consider our Google Search experience, way earlier on, we had to build the ML and AI foundation to return contextually relevant answers and understanding patterns in user behavior so that we can provide the top results. For Android, we had to build the infrastructure for mobile experiences. For Chrome, the foundation for secure devices. For the Maps, we built the foundation for location data and APIs. And for YouTube, we built the infrastructure for video. All these are good examples to say that we had to build an end-to-end secure and scalable infrastructure to handle the scale in complexity. And now through Google Cloud, we work very deeply with the ecosystem, with the community in externalizing these tools so that you can make your information, make it accessible and useful. And thanks to advances in AI, Google is moving beyond our core mission of organizing the world's information and being an AI-first company. And what I mean by that is we want our products to work harder for you in the context of your life, your job, your home. It is equally incumbent on us to make sure that technology is harnessed for good and available to everyone. Here's an example. AI in healthcare needs indeed holds great promise for social good. Let me begin my talk with an inspiring example that really hits a tone. I'll play a video. No one's ever collected large data sets of people whose speech is hard for others to understand. They're not used in training the speech recognition. The game is to record things and then have it recognize things that you say that aren't in the training set. Dimitri recorded 15,000 phrases. It wasn't obvious that this was going to work. He just sat there and he kept recording. You can see that it's possible to make a speech recognizer to work for Dimitri. It should be possible to make it work for many people, even people who can't speak because they've lost the ability to speak. The work that Shansheng has done on voice utterances, from sounds alone, you can communicate. There might be other ways of communicating. Most people with ALS end up using an on-screen keyboard and having to type each individual letter with their eyes. For me, communicating is slow. Steve might crack a joke and it's related to something that happened a few minutes ago. The idea is to create a tool so that Steve can train machine learning models and stuff to understand his facial expressions. To be able to laugh, to be able to cheer, to be able to boo. Things that seem maybe self-influenced but actually support to being human. I still think this is only the tip of the iceberg. We're not even scratching the surface yet of what is possible. If we can get speech recognizers to work with small numbers of people, we'll learn lessons which we can then combine to build something that really works for everyone. To understand and re-understood the cultures of being human. At its core, Google embodies helpfulness. In healthcare, one in 70 searches are healthcare-related. Healthcare information influences all of our lives with significant opportunities for improvements in both personal wellness and decision-making. Let me give you some examples that during COVID, how our mission transcended to providing user information to help them keep safe, informed, and connected during a rapidly evolving period of uncertainty. As I mentioned, Google was founded on the idea that bringing more information, organizing it, and making it accessible to more people improve lives on a vast scale. In this historic moment, access to right information at the right time will save lives. And as this mission transcended to keeping people safe, informed, and connected, we walked to the public health authorities like CDC and WHO. In the beginning of the pandemic, we had over 400 billion impressions of, about COVID in our search. And we also took down 850,000 videos about it that we felt were harmful. As we move out of the pandemic, we'll continue to put the best, most accurate content forward in three important ways. First is amplifying authoritative voices through YouTube, curating the search results, and also combating false information. Now, shifting gears, to say that COVID-19 has been transformational for the healthcare industry would be a significant understatement. As we look back past 18 months, we are seeing incredible changes that were happening at a remarkable speed. It's been inspiring to see how healthcare ecosystem, payers, provider life sciences have reinvented themselves over past 18 months. We have seen unprecedented collaboration in the ecosystem, like never before. Caregivers were connecting with patients through telehealth and supporting communities through secure digital therapies and processes. While providers were offering new ways to support our frontline heroes in coping with the stress of pandemic. We also saw the critical role of data and technology that played across the industry from COVID-19 research to vaccine distribution and even policy decision. It's been a humbling challenge for all of us, and I continue to be inspired by the work and the ecosystem and how we are all navigating through it, despite these challenges. Our focus at Google Cloud quickly became helping the ecosystem, navigating the impact of COVID-19. Whether it was doing employee work remotely, supporting education in school using Google Classroom, making social services accessible or making sure communities can access latest information on the virus. But also these four areas where we were helping our customers, our community and the ecosystem. One is around data interoperability and insights. We all saw interoperability became the key point in COVID-19. And in healthcare, this remains a big challenge. How do you connect different data silos? We have launched healthcare data engine that takes a variety of inputs and harmonizes and normalizes and creates a patient longitudinal record. There are examples I'll share with Mayo Clinic, how they're building on top of that to use AI both in clinical settings, helping physicians and caregivers. The second area is around, we have seen tremendous progress in digital front door solutions. We have seen across the globe, healthcare organizations are developing innovative hybrid healthcare models. During COVID, while telehealth took off, we do believe that as we navigate to the new normal, the approach is going to be much more hybrid. It's clear that healthcare is not going to happen just in the four walls of the hospitals, but also 24 by seven. And approach both between physical and the digital world. One good example to share is Chirality. They are in Latin America. During pandemic, they built a virtual health platform that delivers more than 300,000 medical appointments in a month and almost 3 million virtual consultation in Columbia. I'll also share an example where how remote diagnostics are becoming a reality. Thirdly, I want to touch upon the technology is not just in looking at a transformation, but clinical pandemic shown a bright light on the cracks and weaknesses in the healthcare supply chain. Demonstrating the need for more data visibility into systems and ways of managing spikes in demand. One good example is Moderna. They used our Google Cloud Looker in really streamlining the access and created a dashboard that helped them, their logistics team to optimize shipment for the budget goal for the 60,000 plus shipments per year. And finally, on the research side, we are all seeing and witnessing technology machine learning could play an important role in accelerating the discovery of new medicines. Now, one another example here is we, during pandemic, Google, we worked with Harvard Global Health Institute and created COVID-19 public forecast. We also made available the community mobility reports data sets that are available for you to combine with your own unique data sets. Another good example is our DeepMind, our colleagues there, they had a breakthrough in AlphaFold, which is a 50-year-old grand challenge of protein structure prediction. And this is a stunning breakthrough, and the good news is now this data will be freely and openly available to the scientific community. It will cover all 20,000 proteins expressed by human genome. So next I'll show you some examples of how other ecosystem partners have leveraged our technology and are advancing and pushing both academic science and research and bringing that to clinical workflow. This first example is with Mayo Clinic. So what you're seeing here is they are using Google Cloud for auto-contouring of certain type of cancers. Radiotherapy, we all know, is used in the treatment of approximately 50% of cancer in the U.S. Mayo Clinic has a world-leading expertise for contour planning as to where the radiation needs to be delivered. It takes three to four hours, sometimes longer per case, and is error-prone, reducing access to curative treatment. Additionally, the number of patients requiring radiotherapy globally exceeds the supply of clinical experts capable of planning this treatment eminently. Lastly, quality of therapy varies by location, especially for remote sites. So with our partnership with Mayo Clinic, it allowed them to build segmentation models capable of contouring medical images and integrate those AI-generated contours into the clinical workflow so that a radiotherapy team can leverage the auto-contouring tool to improve accuracy and reduce time to treatment. In this visual that you're seeing on the screen, as the CT scan goes from right to left, the algorithm at the back automatically segments various body organs and contours them for radiological view on the left. The physician will be able to interact with these images, improve accuracy of diagnosis, and significantly reduce time to treatment. While this is important, there will be 25 million new cancer cases worldwide by 2030. So the need for the solution will only grow in the next decade, and there's an opportunity to save lives for low- and middle-income countries from improved access and speed of radiotherapy. Another example I want to show how technology and AI can help democratize access to healthcare and lower the barrier. This is an example. Let me play a video. My husband used to say, no, it doesn't cost anything, you don't have to pay anything. Thinking about people in need, we had the idea of opening a clinic. Amazonas is another country within Brazil. I'm here practically in geographic isolation, where in my city I only go by plane or boat. We have more than 200 villages that are far away. Few specialists want to come and live in Amazonas. We partnered with Telemedicina. Portal is a telediagnostic platform, so we connect remote areas like the Brazilian rainforest to specialist doctors in São Paulo. We receive EKGs, EGs, and X-rays, and then do the diagnostics online and get this back to these remote areas. The conventional way usually takes 60 days to get a result back, and that's hundreds of dollars in cost. And we do this in the same day, in a few minutes, with 10x less cost. So for us, the cloud was the only way. It needs to be easier to use. A nurse in the middle of the rainforest is not an IT technician. It will go to the cloud and to the doctor. So Google Cloud is enabling us to really grow and operate this large-scale telemedicine company. We have our first client in Africa now, in Angola, which is sending EKGs to Brazil, and our cardiologists are giving the results back there. So we can launch in Europe, the US, Africa, and the whole globe, I hope. The end goal is that anyone in the world can have access to quality healthcare through Google Cloud. And I believe that this will change healthcare forever. I was helped a lot in my life. So you have to think about the next one. You have to think about the human being. We've been together for over 20 years. Sorry about that. So at the end, Google is an information company at heart, and we have expertise in organizing complex information into intuitive, useful formats. We are a team of engineers, clinicians, researchers, and other experts applying Google's expertise in AI, product innovation, and hardware to take on big health challenges. But there's tremendous potential for AI in healthcare. We will continue to work across our teams and products, but we have to do this in close collaboration with all of you, with experts like yourselves across the industry, as well as those directly delivering and receiving care. We've also seen pandemics shine bright light on our healthcare disparities. They are real. Our battle is not just with COVID. It's also with the inequitable health access and inequity. So as I close, there's a call to action for all of you working in healthcare ecosystem, be on the reset side, be on the care delivery side, in doing a part in our work by looking at the larger context in which people are born, live, work, play, and pray. This will give rise to research and healthcare system that increasingly focuses on health and well-being for everyone. Thank you for affording me this opportunity to share some of our vision with all of you. Thank you so much.

collaboration

Google Cloud

AI

machine learning

healthcare industry