How is artificial intelligence reshaping healthcare? How is technology driving the industry toward precision medicine? How can healthcare companies leverage existing tools and resources and incorporate artificial intelligence into their work?
These are some of the questions discussed at NVidia’s GTC Conference, an annual conference on artificial intelligence that brings together NVidia leaders and the global community of innovators, developers, engineers, researchers and more. The Black Opal Ventures team attended the virtual conference’s healthcare track in early November, 2021. Here are some of our key takeaways:
Healthcare is generating the largest amount of data of any industry According to Kimberly Powell, Vice President of Healthcare at NVidia, healthcare data is 30% of the world’s data and by 2025 healthcare data will grow at a rate higher than any other industry. This vast size of data offers tremendous opportunity to improve health outcomes and healthcare delivery. Powell shared a few key facts: hospitals generate 50 petabytes of data each year, there are more than 2 million device types, and over 16 thousand drugs are in development. She estimates that over 10 million artificial intelligence models are needed to optimally leverage healthcare data.
The use of data is enormous and growing. FDA approvals for software as a medical device have increased from 30 in 2015 to 300 in 2020. Deep learning and machine learning research is exploding with over 2500 papers published each month on these topics. And NVidia has seen a doubling of start-ups in their Inception program from 700 in 2019 to 1400 in 2021.
Transformation of visual data is changing fields like Radiology and Pathology A number of presentations focused on the use of visual data for precision diagnostics and treatment. For example, Mengling Feng of the National University of Singapore described work his team is doing to improve mammogram accuracy for breast cancer screening. IBEX shared their product to improve pathology readings for the diagnosis of cancer. And Rayshape shared their early phase product that uses AI for ultrasounds. These are just a few examples of companies that are using visual health data for precision screening and diagnosis.
Dr. Michal Guindy, Head of Imaging at Assuta Medical Centers, described radiology and pathology as good service lines to adopt AI because they are highly computerized fields and large data are available without language barriers. She described three value propositions of AI in radiology: increases productivity, quantification of imaging, and improved accuracy and precision.
Artificial Intelligence is being used to improve clinical decisions across the continuum of care Throughout the conference, it was clear that artificial intelligence can be used throughout the continuum of care from prevention to diagnosis to treatment. Eran Rubens, the Innovation Strategy Leader at Phillips, discussed the wholistic approach that Phillips uses across entire patient journeys. “When we talk about AI in radiology or in imaging, most people think of automated detection and findings on the image but actually there is so much more that AI can do throughout the patient’s journey,” said Rubens.
Others spoke about the need to integrate AI into healthcare systems processes in order for them to have an impact. David Golan, Founder of Viz.ai described AI-driven triage for patients with suspected strokes. Not only has the use of AI synchronized workflows to shorten time to care but it has also improved patient outcomes.
AI is being used at the point of care in surgical procedures Robotic surgery has been used for over twenty years with surgeries like prostate removal and gynecologic surgery having large market penetration. We are now seeing next generation robotic surgery that leverage the power of artificial intelligence. A team from Medtronic spoke about Research and Development in real-time surgical analytics such as the use of AI to identify organ structures and surgical margins. They emphasized the need for cutting-edge technology such as sensors and edge compute.
As a physician and healthcare executive who started her career when radiology was read on bulky films placed over light boxes, I am excited about the prospect of more precise, personalized care and greater system efficiency through the use of AI. Of course, start-ups and healthcare organizations need to consider the risks and challenges of AI in healthcare, including data security, patient privacy, errors and accountability, and integration into real-life processes. But I believe that companies that can develop cutting-edge artificial intelligence, figure out how to seamlessly integrate their use in healthcare setting, and demonstrate value through better health outcomes at lower cost will be gamechangers in healthcare.
This article was originally published on Medium
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