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Building Trust and Adoption in Machine Learning in Healthcare
A Medium publication from @HarryCGoldberg on ML in healthcare
Note from the editor

This Medium publication openly shares the learnings from my UC Berkeley MPH capstone project. The goal of the project was to gain a better understanding of how clinicians perceive ML in healthcare and to explore what ML tool developers, such as product managers and ML engineers, can do to build trust and adoption. The hope is that, as Dr. Eric Topol states in his book Deep Medicine, ML will unlock time and mental space for clinicians to focus on the human aspects of healthcare. This work aims to build on previous research on frontline US clinicians’ knowledge and feelings toward ML tools. Yet, it goes beyond broad surveys and focuses on in-depth interviews with clinicians from diverse backgrounds. These frontline clinician perspectives complement insights from clinical administrators, ML tool developers / researchers, and patients. Yet, with clinicians being the primary users of many emerging use cases of ML in clinical healthcare delivery, their voices should be heard and aspects of the technology that make them excited and concerned should be understood. I hope you enjoy it!

Editors
Go to the profile of Harry Goldberg
Harry Goldberg
Beyond healthcare ML research, I spend time as a UC Berkeley MBA/MPH, WEF Global Shaper, Instant Pot & sous vide lover, yoga & meditation follower, and fiance.
Writers
Go to the profile of Harry Goldberg
Harry Goldberg
Beyond healthcare ML research, I spend time as a UC Berkeley MBA/MPH, WEF Global Shaper, Instant Pot & sous vide lover, yoga & meditation follower, and fiance.