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Davit Buniatyan is the CEO and Founder of Activeloop, a company advancing the way deep learning teams manage multi-modal datasets. In this episode, Buniatyan discusses how Activeloop's AI-native data format based on tensors is poise to help transform healthcare by enabling the digitization of medical images and streamlining workflows.
Buniatyan shares insights into the primary safety concerns when integrating private company data with large language models, as well as the current challenges around the GPU shortage and how Activeloop is addressing them. He also delves into the rise of unstructured data and the need for industry standards to efficiently process this information for machine learning purposes.
As the leader of a company at the forefront of AI data management, Buniatyan provides valuable perspectives on the future of this rapidly evolving field and how Activeloop is helping teams ship AI products faster without the need to build complex data infrastructure.
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In this episode, we explore the pioneering world of digital and computational pathology with Dr. David Klimstra, whose illustrious career spans from his former role as Chair of the Pathology Department at Memorial Sloan Kettering Cancer Center to his current position as Co-Founder and Chief Medical Officer at Paige AI. Dr. Klimstra shares his journey through the evolution of pathology, highlighting the transformative impact of artificial intelligence and digital technologies on the field. We explore how his work at Paige AI is reshaping the landscape of cancer diagnosis and treatment, making it more precise, efficient, and patient-focused.
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Episode 37: AI for psychotherapy. With Lindsay McKean, Psychotherapist
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The liver is a miracle of physiology, with over 1,000 known functions. This highlights the potential impact of liver dysfunction on various bodily systems. Also, liver disease is a major cause of death globally, and the numbers are on the rise. This adds up to a critical clinical need.
Dr Neil Guha is Professor of Hepatology at the University of Nottingham, an NHS consultant and Research scientist. He recently published an excellent paper in the journal of medical AI on a ML model to rate risk for liver disease.
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A year ago, very soon after the release of ChatGPT there was an article in Nature with the curious title… ChatGPT listed as author on research papers: many scientists disapprove
Since then, the use of LLMs in medical research has exploded, including as a coauthor of articles. But how is it being used? Does it matter? Does it undermine the human authors? And does it risk the quality of the research?
With me to discuss this is a brilliant up and coming researcher at the Universal Scientific Education and Research Network, Fiona Morrison.
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Anitha Kannan is a founding member and the head of machinelearning at Curai Health, an AI-powered virtual clinic on a mission to improveaccess to care at scale. Anitha spearheads the development of machine learningtechnology designed to reimagine care delivery and enhance patient outcomes.Prior to her work at Curai, Anitha held senior research positions at FacebookAI Research and Microsoft Research, where she received multiple Gold Starawards. She holds a PhD in machine learning from the University of Toronto andwas a Darwin Fellow at the University of Cambridge.
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Dr. Dan Heller leads the Cancer Nanomedicine Laboratory at Memorial Sloan Kettering Cancer Center, where he develops advanced diagnostic technologies.
His lab recently made headlines for creating a nanosensor "nose" that can sniff out ovarian cancer sooner than current tests. Using carbon nanotubes and machine learning, this artificial olfaction technology was able to identify ovarian cancer in early stages, making treatment more feasible.
In addition to his pioneering work in the lab, Dr. Heller co-directs the Pat and Ian Cook Doctoral Program in Cancer Engineering at Memorial Sloan Kettering, is a member of the Molecular Pharmacology Program at the Sloan Kettering Institute, and serves as a Professor at the Weill Cornell Graduate School of Medical Sciences.
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Personalised medicine has been promised as a coming revolution in healthcare. The concept goes all the way back to Hippocrates, around 400 years BC, but it is still very rare in clinical practice. Machine learning has been described as the key technology for personalised medicine - so are we finally about to see this revolution storming the gates? Discussing this with Felix is one of the key figures in this area, Richard Dobson, Professor of Medical Informatics at the Institute of Psychiatry, Psychology & Neuroscience, and Professor of Biomedical and Health Informatics at the Institute of Health Informatics, University College London.
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Are there ethical issues with medical AI that are unique? Any major technological development can have huge benefits and huge risks. And medical AI is no different. The benefits of medical AI are talked about endlessly. Much less so the risks. But what are those dangers?
The Declaration of Helsinki is widely regarded as the cornerstone of human research ethics. This a set of ethical principles regarding human experimentation developed originally in 1964 for the medical community by the World Medical Association. The fundamental principle is respect for the individual, his or her right to self-determination and the right to make informed decisions regarding participation in research. But what is the support adn the Declaration of Helsinki? Is it for purpose? Or is it actually highly questionable in some circumstances?
Joining Felix is Professor Erwin Loh, Chief Medical Officer for St Vincent's Health, Australia’s largest not-for-profit health care provider, Professor at Monash, Melbourne and Macquarie Universities and renowned author on medical AI.
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Medical AI might seem like a really new field. For example, the first medical AI product approved by the FDA was done so in only 2017. But does medical AI actually have a history that is far longer and richer than we might think? And are there important lessons to be learnt from that history?
Felix discusses with Professor Mark Braunstein, Author of "Health Informatics on FHIR". An author and thought-leader in the field, Dr. Braunstein is a professor of health informatics for the School of Interactive Computing in the College of Computing at the Georgia Institute of Technology where he teaches Introduction to Health Informatics, a popular graduate seminar, both on campus and as part of the OMSCS program. He is involved with HL7 and in various research projects at Georgia Tech, and works with and advises numerous community and industry groups.
Original music by Felix Beacher: https://youtu.be/JS93808r-W4
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There are many research groups and companies developing excellent products in medical AI. But some succeed and some fail. What is the difference between the two? Felix discusses this with someone who is a real expert in this - Dr Hugh Harvey Managing Director of Hardian Health. Pure gold for anyone interested in developing a strategy for new medical AI products.
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Worldwide, the capacity of health systems is being strangled, by the complex healthcare needs of ageing populations, and the rise in chronic diseases more generally. Radiology departments in particular, are simply unable to meet a massive and rising demand and patients are facing ever longer delays in treatment. Much has been made about the ability of AI systems to interpret medical images as well or better than human radiologists. But are we really moving towards a future in which AI systems have transformed radiology to the point where they are independently processing scans quickly, accurately and en masse? If that transformation is underway, what will be the impact, good and bad? With Fabian Schoeck, Head of Global Product Management for AI Products at Siemens Healthineers.
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GE Healthcare is the market leader in medical AI: it has the largest market shares and the largest number of FDA-approved medical AI products. This includes algorithms for MRI image cleaning, detection of collapsed lungs and diagnosis of breast cancer. So where GE Healthcare is going with medical AI really counts. To discuss this I am joined by Simon Rost, Chief Marketing Officer of GE Healthcare.
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Medical ultrasound is now old technology – its first use was in 1941 for detecting brain tumors. However, it is also a technology with a great future and AI is a big part of that evolution. Felix is joined by Dr Yoran Hummel president of Us2.ai to discuss current AI-based innovations for ultrasound imaging.
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