Verily Life Sciences and Machine Learning

Verily Life Sciences and Machine Learning

Kevin A. Schulman, Kevin Ho
2020|Case No.SM335| Length 22 pgs.

Verily Life Sciences, an independent subsidiary of Alphabet, Inc., set out to harness machine learning in the health care field. The company sought partnerships with academic research institutions, legacy life sciences companies, and hospitals and health systems to develop tools to collect and organize health data, with the goal of creating platforms that utilized the insights from that data to enhance patient care. The case study discusses these broad partnerships and goals, the illness-specific health monitoring and care tools in Verily’s project pipeline, and efforts by competitors like Apple and Amazon— as well as a growing number of start-ups. The case provides insights into decision making in the largely uncharted territory of machine learning in the health care industry, providing an in-depth look at Verily’s diabetic retinopathy project, which screened for eye disease.

Learning Objective

The case study aims to expose MBA and other students in the health care industry to broad opportunities in machine learning, as well as the many questions a company might face in developing a business model in a largely new field that was attracting a growing number of competitors. How would companies like Verily acquire and curate millions of piece of data, and develop potentially life-saving technologies? How would they monetize and market these algorithms?

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