We talk about ML and AI quite a bit on this show. Our angle is always to avoid the hype and help you find the practical applications that you can put to work right now. Today’s episode is a great extension of this ongoing conversation. We talk with Dr. Taha Kass-Hout, Chief Medical Officer and Director, Machine Learning at Amazon Web Services about how they are trying to bring ML to the masses. We discuss:
- What’s needed to use machine learning at scale with cost efficiency, performance, and accountability.
- The challenges in analyzing healthcare’s largely unstructured data.
- The practicality of bringing AI/ML capabilities directly to the hospitals, health systems, medical offices, etc.
- How should CIOs and other buyers evaluate ML solutions? How can they tell what is real and what is hype?
- Detecting bias, drift in models, and drift in data in machine learning.
- Tooling for privacy, security, and compliance.
- The importance of owning and controlling your own data.
- The problems caused by bad training data
Dr. Taha Kass-Hout
Dr. Taha Kass-Hout, M.D., M.S., is the Chief Medical Officer and Director, Machine Learning, Amazon Web Services, and leads our Health AI strategy and efforts, including Amazon Comprehend Medical and Amazon HealthLake. He works with teams at Amazon responsible for developing the science, technology, and scale for COVID-19 lab testing, including Amazon’s first FDA authorization for testing our associates—now offered to the public for at-home testing. A physician and bioinformatician, Taha served two terms under President Obama, including the first Chief Health Informatics officer at the FDA. During this time as a public servant, he pioneered the use of emerging technologies and the cloud (the CDC’s electronic disease surveillance), and established widely accessible global data sharing platforms: the openFDA, which enabled researchers and the public to search and analyze adverse event data, and precisionFDA (part of the Presidential Precision Medicine initiative). Taha holds doctor of medicine and master of science in biostatistics degrees from the University of Texas and completed clinical training at Harvard Medical School’s Beth Israel Deaconess Medical Center.
About AWS Healthcare, Life Sciences, and Genomics
AWS is helping healthcare, life sciences, and genomics customers around the world transform the way they conduct research, develop products, and enhance the patient experience. AWS currently works with many customers, ranging from small, independent labs and clinics, to researchers, device manufacturers, Fortune 500 companies, and government entities such as Ontario Health, UC San Diego Health, MetroPlus Health Plan, Moderna, Cerner, Philips, and Novartis as well as the industry’s newest innovators such as Halodoc, Lyell Therapeutics, Butterfly IQ, aidoc, Bluedot, and BenevolentAI. AWS offers over 130 HIPAA-eligible services, deep domain expertise, complete industry-specific offerings, access to a vast network of partners, and the security and privacy required by this highly regulated industry to help improve patient care, advance precision medicine, and bring new therapeutics to market faster.
- Twitter: https://twitter.com/aws?lang=en
- LinkedIn: https://www.linkedin.com/company/amazon-web-services/
Links and resources
- AWS for Health Landing Page: AWS for Health
- AWS for Health Blog Post: Introducing AWS for Health – Accelerating innovation from benchtop to bedside
- HealthLake GA Press Release: AWS Announces General Availability of Amazon HealthLake
- AWS COVID-19 Open Research Dataset (CORD-19): AWS launches machine learning-enabled search capabilities for COVID-19 dataset
- Anthem Case Study: Anthem Enables Intelligent Claims Processing Using Amazon Textract
- Cerner Congestive Heart Failure Study with AWS White Paper: Cerner HealtheDataLab Overview and Cerner Press Release: Taking data from big and complex to manageable and actionable
- Episode 162: What’s up with IBM Watson Health? A Discussion on the State of AI in Healthcare with Paddy Padmanabhan
- Episode 159: A Practical Look at Machine Learning in Healthcare with Josh Miramant
- Episode 147: Applying Conversational AI to Reduce Provider Burnout from HealthIMPACT Live
- Episode 116: Enhancing Diagnostic Accuracy with Art Papier of VisualDx
- Episode 91: Deep Medicine with Dr. Eric Topol
- Episode 43: Demystifying Big Data and Machine Learning for Healthcare w/ Prashant Natarajan
Listen to the interview right here:
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Music by StudioEtar