The Genomics team in Google AI develops methods to help understand genomic data and combine it with non-genomic biomedical data. Our team builds DeepVariant, a highly accurate deep learning variant caller. The real world applications are in improving clinical genomics, drug discovery, and in non-human biotechnology. Andrew shapes product strategy to drive adoption of the technology and ensure its connection to high-value application. His role has a strong focus on genomics community engagement, collaboration, and partnership. Read his full bio.

Interview with Andrew Carroll of Google AI

Q: Artificial intelligence (AI) techniques have sent vast waves across healthcare, even fueling an active discussion of whether AI doctors will eventually replace human physicians in the future. Do you believe that human physicians will be replaced by machines in the foreseeable future? What are your thoughts?

A: I believe that applying AI technologies in healthcare will make physicians more valuable, and will make their careers more enjoyable and sustainable. Practicing medicine is not only very demanding on physicians’ time, it also involves huge cognitive overheads from multi-tasking and context switching. Recent trends like Electronic Health Records bring benefits of consolidating information, but at the cost of greater time burdens and context switching.

We will apply AI in Healthcare to organize and integrate streams of information; to help summarize it meaningfully for physicians in ways that allow them to manage complexity. This will allow doctors to have more time and focus for the things which they are uniquely specialized for and want to do: understanding the important concepts of their field, connecting with their patients, and observing the subtle facets of their illnesses.

Even in specialty fields like histology and pathology, where the concern around AI replacing doctors is most pronounced, AI is likely to enhance physicians’ usefulness. In a recent publication from Google AI and collaborators “Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer”, using AI to annotate key areas of slides allows pathologists to reach diagnoses faster and more accurately. This is one power example of humans being stronger with AI as opposed to the idea of competing. We also see this to be true in other domains, including ophthalmology.

In the end, I feel that as we apply AI further into healthcare, it will off-load from doctors the machine components of their job, and ultimately make the practice of medicine more human.

Q: Can you provide some use cases that have already successfully demonstrate the value of AI/Machine Learning in healthcare?

A: AI has already transformed one area of how we relate to health and our healthcare, though I feel few realize it. Right now, AI is an essential technology in organizing and querying information in search engines and phones (such as Google search and Android). These technologies are changing the access that people have to information about how they can stay healthy, how they realize they are sick, and what they can do to recover. These examples show the use of AI in personal settings; as AI is increasingly applied into more technical and specialized domains, it will have similar transformative effects on the information available to health professionals and the simplicity of accessing it.

Q: What areas in healthcare will benefit the most from AI/Machine Learning applications and when will that be?

A: I am uncertain which domains will ultimately receive the greatest impact, but I have a clear idea of the first to benefit: those fields which relate to imaging and natural language processing. These domains have the most mature machine learning infrastructures and community of expert developers. These fields have huge databases of annotated examples from both general and specialized domains, and the AI applications are already of profound value in non-specialist commercial systems. We can expect progress in areas like understanding, summarizing, and annotating speech, text, images, and video to continue to advance rapidly.

This will provide the foundation for applications which organize, summarize, and annotate medical imaging, doctor’s transcriptions, unstructured and structured information in the EHR, and medical billing.

Q: What are some of the challenges to realize AI/Machine learning in healthcare?

A: One of the biggest points of resistance I hear from the community about AI is a perceived lack of explainability and, behind that, a hesitation to trust AI. This is something the AI community will have to address, as no matter how capable we make the technology, it is ultimately applied through the hands of people who must trust it.

The good news is that AI systems are not fundamentally un-explainable. There are many techniques which allow AI systems to show why they are making the decisions they do. One example is a recent Google AI publication “Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning”. This demonstrated that, amazingly, deep learning models can predict from eye images a person’s age, sex, BMI, smoking status, or cardiovascular health with high reliability. The models were also designed to highlight the most informative features for these predictions, allowing the model to specify what parts of the eye relate to these traits, and in turn suggesting links to the underlying biology.

As a field, we must continue to ground our AI systems and predictions in the underlying biology. Because deep learning models act on relatively raw information, they often construct models of problems independent from human characterization of the field. When we work to ground these models in biology, not only do we provide a check on their validity, we can also use them as a check on our understanding. This can even discover new insight we didn’t know was there.

Q: What are the products and/or services Google AI offers/develops in the AI/Machine Learning sector? What makes Google AI unique?

A: My team within Google AI makes a tool called DeepVariant, which processes sequencing data to identify the positions in the genome which make an individual unique. DeepVariant is highly accurate, having won an award in the 2016 PrecisionFDA competition for highest SNP accuracy. DeepVariant is also high extensible, because it can be quickly improved or changed by retraining with new data as opposed to needing extensive coding for a new problem type.

DeepVariant is also an open-source tool. Anyone is free to download the code and modify it. Anyone is free to train their own models for it, whether for a different sequencing technology or tuned for a specific organism. DeepVariant is itself built on top of TensorFlow, which is an open-source library for deep learning also developed by Google in an open-source manner. I am grateful for Google’s commitment to open-source in these foundational technologies.

Q: What is your role at Google AI and what excites you about your work?

A: I have always been fascinated by both biology and AI. I started working in genomics around the time that the cost of sequencing fell dramatically and whole human genomes started to be something you could do at scale. It was amazing to watch the field of genomics grow and change so quickly. Sometimes people would ask about where a given technology would be in ten years and I would have to remind them that ten years ago, none of the technologies we are using had been invented.

More recently, I feel the same is true about Machine Learning and AI. It reminds me of that same time when the first large genomics cohorts were being sequenced. I feel so lucky to be at the intersection of not just one, but two fields that are actively re-inventing themselves.

I also feel the energy of a community which sees the potential in the combination of AI, genomic, and healthcare to change our lives for good. That these are essential tools which help us understand what makes us sick and healthy, so that we steer ourselves toward health and happy lives.

Interview with Gabriel Bien-Willner of Palmetto GBA

Q: What does your role entail as the director of the MolDX program at Palmetto GBA?

A: The job directing MolDX is multifaceted; first and foremost the MolDX program is responsible for assessing molecular diagnostic tests on the market and makes coverage and pricing determinations for such tests and technology. This is usually done through local coverage determination policies or technical assessments.

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Interview with Peter Marks of FDA

Q: The CBER’s Regenerative Medicine Advanced Therapy Designation program has been very successful, with about 100 requests for designation in the two years of its existence. Can you please tell us about the program and how it was put together?

A: The Regenerative Medicine Advanced Therapy (RMAT) Designation program came into being as part of the 21st Century Cures Act that was signed into law on December 13, 2016.

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Interview with Calum MacRae of Harvard Medical School

Q: What patient data do we need to better understand the underlying cause of disease and how to prevent it?

A: Medicine at present is highly underdetermined and data poor. To be precise, one must be comprehensive, so medicine (with our consent) will use not only what we currently conceive of as biomedical information, but also data from across our lives.

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Headlines from PMWC 2019 Silicon Valley

A big ‘Thank You’ to all of our presenters and attendees for celebrating 10 years of precision medicine progress with us! PMWC 2019 Silicon Valley was attended by 2000 participants from 35 countries, which included over 400 speakers in 5 parallel tracks!

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Interview with Ken Bloom of Ambry Genetics

Q: Tell us more about your organization/company. What patient population are you serving and which services are you specializing in?

A: Ambry Genetics is a recognized leader in high quality complex genetic testing. We seek to find the genomic cause or contributors to rare diseases, abnormal phenotypes and hereditary disorders.

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Interview with Lee Pierce of Sirius Computer Solutions

Q: What is the state of big data and analytics in healthcare, and how to best use the reams of data available?

A: More than ever, Healthcare organizations are achieving measurable value through use of their data and analytics assets. There is more raw material available than ever to create value. This raw material is the data flowing from internal systems and applications and also from devices and systems external to healthcare organizations.

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Interview with Anita Nelsen of PAREXEL

Q: There are various new, emerging technologies that bring us closer towards a cure for life-threatening disorders such as cancer, HIV, or Huntington’s disease. Prominent examples include the popular gene editing tool CRISPR or new and improved cell and gene therapies. By when can we expect these new technologies being part of routine clinical care?

A: Today’s emerging technologies are making the promise of individualized treatment a reality.

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Interview with Ilan Kirsch of Adaptive Biotechnologies

Q: The Nobel Prize in Medicine was awarded recently to James Allison and Tasuku Honjo for their work on unleashing the body’s immune system to attack cancer, a breakthrough that has led to an entirely new class of drugs and brought lasting remissions to many patients who had run out of options. The Nobel committee hailed their accomplishments as establishing “an entirely new principle for cancer therapy.” What is your first-hand experience the impact that those new drugs had on patients?

A: For decades cancer was viewed as solely a cell-autonomous condition.

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BMS buys Celgene | Lilly buys Loxo Oncology – Does this Signal a Return to Strong Deal-Making Activities in 2019?

Bristol-Myers Squibb’s blockbuster $74B deal to buy Celgene creates an oncology powerhouse amid industrywide excitement about the rapidly evolving science and explosive growth of the sector. The agreement could signal a return to deal-making for the pharmaceutical industry in the $133B global oncology therapeutics market.

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Interview with Gini Deshpande of NuMedii

Q: What need is NuMedii addressing?

A: NuMedii, has been pioneering the use of Big Data, artificial intelligence (AI) and systems biology since 2010 to accelerate the discovery of precision therapies to address high unmet medical needs. Artificial Intelligence approaches are a natural fit to harness Big Data as they provide a framework to ‘train’ computers to recognize patterns and sift through vast amounts of new and existing genomic

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Interview with Minnie Sarwal of UCSF

Q: Genomic medicine is entering more hospitals and bringing with it non-invasive technology that can be used to better target and treat diseases. What are some key milestones that contributed to this trend?

A: Completion of complete sequence data from the human genome project, and the advances in proteomic, microRNA and epigenetic assays added a layer of pathway biology to the understanding of human diseases.

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Interview with Shidong Jia of Predicine

Q: Once sequencing has been validated as a clinical solution via trusted workflows, and coinciding with the technological developments driving costs lower, we can expect accelerated human genome profiling for clinical Dx. How soon, do you think, will we see accelerated growth and what can we expect?

A: We will see accelerated human genome profiling for clinical Dx in 2019 and the coming years as more biomarker-based cancer drugs are gaining approval.

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Interview with Iya Khalil of GNS Healthcare

Q: Artificial intelligence (AI) techniques have sent vast waves across healthcare, even fueling an active discussion of whether AI doctors will eventually replace human physicians in the future. Do you believe that human physicians will be replaced by machines in the foreseeable future? What are your thoughts?

A: I think that there’s a lot of speculation and uncertainty around AI, but I don’t foresee a time when we won’t need physicians.

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Interview with Ilya Michael Rachman of Immix Biopharma Inc.

Q: The Nobel Price in Medicine was awarded recently to James Allison and Tasuku for their work on unleashing the body’s immune system to attack cancer, a breakthrough that has led to an entirely new class of drugs and brought lasting remissions to many patients who had run out of options. The Nobel committee hailed their accomplishments as establishing “an entirely new principle for cancer therapy.” Besides CAR T-cell therapy what do you think next generation immunotherapies will look like to successfully combat cancer?

A: The next generation of immunotherapies will build on the insights discovered by immunologists like James Allison and Tasuku Honjo and extend them to modify the body’s response to tumors.

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Join me to Kick off PMWC Silicon Valley in the Santa Clara Convention Center, Focusing on Every Element of Precision Medicine

My team worked in collaboration with Bill Dalton, Kim Blackwell, Atul Butte / India Hook Barnard, Nancy Davidson and Sharon Terry to create a program that touches every component of precision medicine while bringing together all of its key stakeholders. Leading participating institutions including Stanford Health Care, UCSF, Duke Health, Duke University, John Hopkins University, University of Michigan and more will share their learnings and experiences and their successes and challenges, as they make precision medicine the new standard of care for all.

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Johns Hopkins
University Of Michigan

The Precision Medicine World Conference (PMWC), in its 17th installment, will take place in the Santa Clara Convention Center (Silicon Valley) on January 21-24, 2020. The program will traverse innovative technologies, thriving initiatives, and clinical case studies that enable the translation of precision medicine into direct improvements in health care. Conference attendees will have an opportunity to learn first-hand about the latest developments and advancements in precision medicine and cutting-edge new strategies and solutions that are changing how patients are treated.

See 2019 Agenda highlights:

  • Five tracks will showcase sessions on the latest advancements in precision medicine which include, but are not limited to:
    • AI & Data Science Showcase
    • Clinical & Research Tools Showcase
    • Clinical Dx Showcase
    • Creating Clinical Value with Liquid Biopsy ctDNA, etc.
    • Digital Health/Health and Wellness
    • Digital Phenotyping
    • Diversity in Precision Medicine
    • Drug Development (PPPs)
    • Early Days of Life Sequencing
    • Emerging Technologies in PM
    • Emerging Therapeutic Showcase
    • FDA Efforts to Accelerate PM
    • Gene Editing
    • Genomic Profiling Showcase
    • Immunotherapy Sessions & Showcase
    • Implementation into Health Care Delivery
    • Large Scale Bio-data Resources to Support Drug Development (PPPs)
    • Microbial Profiling Showcase
    • Microbiome
    • Neoantigens
    • Next-Gen. Workforce of PM
    • Non-Clinical Services Showcase
    • Pharmacogenomics
    • Point-of Care Dx Platform
    • Precision Public Health
    • Rare Disease Diagnosis
    • Resilience
    • Robust Clinical Decision Support Tools
    • Wellness and Aging Showcase

See 2019 Agenda highlights:

    • Five tracks will showcase sessions on the latest advancements in precision medicine which include, but are not limited to:
      • AI & Data Science Showcase
      • Clinical & Research Tools Showcase
      • Clinical Dx Showcase
      • Creating Clinical Value with Liquid Biopsy ctDNA, etc.
      • Digital Health/Health and Wellness
      • Digital Phenotyping
      • Diversity in Precision Medicine
      • Drug Development (PPPs)
      • Early Days of Life Sequencing
      • Emerging Technologies in PM
      • Emerging Therapeutic Showcase
      • FDA Efforts to Accelerate PM
      • Gene Editing / CRISPR
      • Genomic Profiling Showcase
      • Immunotherapy Sessions & Showcase
      • Implementation into Health Care Delivery
      • Large Scale Bio-data Resources to Support Drug Development (PPPs)
      • Microbial Profiling Showcase
      • Microbiome
      • Neoantigens
      • Next-Gen. Workforce of PM
      • Non-Clinical Services Showcase
      • Pharmacogenomics
      • Point-of Care Dx Platform
      • Precision Public Health
      • Rare Disease Diagnosis
      • Resilience
      • Robust Clinical Decision Support Tools
      • Wellness and Aging Showcase
  • Luminary and Pioneer Awards, honoring individuals who contributed, and continue to contribute, to the field of Precision Medicine
  • 2000+ multidisciplinary attendees, from across the entire spectrum of healthcare, representing different types of companies, technologies, and medical centers with leadership roles in precision medicine
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