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Tuesday, March 07, 2023

Google's Looks beyond Health Research

Google outlines some of their research, including the use of AI methods. 

Google Research, 2022 & beyond: Health in the GoogleBlog

THURSDAY, FEBRUARY 23, 2023

Posted by Greg Corrado, Distinguished Scientist, and Yossi Matias, VP Engineering and Research, Google Research

(This is Part 8 in our series of posts covering different topical areas of research at Google. You can find other posts in the series here   .)

Google’s focus on AI stems from the conviction that this transformational technology will benefit society through its capacity to assist, complement, and empower people in almost every field and sector. In no area is the magnitude of this opportunity greater than in the spheres of healthcare and medicine. Commensurate with our mission to demonstrate these societal benefits, Google Research’s programs in applied machine learning (ML) have helped place Alphabet among the top five most impactful corporate research institutions in the health and life sciences publications on the Nature Impact Index in every year from 2019 through 2022.

Our Health research publications have had broad impact, spanning the fields of biomarkers, consumer sensors, dermatology, endoscopy, epidemiology, medicine, genomics, oncology, ophthalmology, pathology, public & environmental health, and radiology. Today we examine three specific themes that came to the fore in the last year:

· Criticality of technology partnerships

· Shift towards mobile health

· Generative ML in health applications

In each section, we emphasize the importance of a measured and collaborative approach to innovation in health. Unlike the “launch and iterate” approach typical in consumer product development, applying ML to health requires thoughtful assessment, ecosystem awareness, and rigorous testing. All healthcare technologies must demonstrate to regulators that they are safe and effective prior to deployment and need to meet rigorous patient privacy and performance monitoring standards. But ML systems, as new entrants to the field, additionally must discover their best uses in the health workflows and earn the trust of healthcare professionals and patients. This domain-specific integration and validation work is not something tech companies should embark upon alone, but should do so only in close collaboration with expert health partners.   .... ' 

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