Organizational Governance of Emerging Technologies: AI Adoption in Healthcare

no code implementations • 25 Apr 2023 • Jee Young Kim, William Boag, Freya Gulamali, Alifia Hasan, Henry David Jeffry Hogg, Mark Lifson, Deirdre Mulligan, Manesh Patel, Inioluwa Deborah Raji, Ajai Sehgal, Keo Shaw, Danny Tobey, Alexandra Valladares, David Vidal, Suresh Balu, Mark Sendak

This is one of the most detailed qualitative analyses to date of the current governance structures and processes involved in AI adoption by health systems in the United States.

Ethics

Development and Validation of ML-DQA -- a Machine Learning Data Quality Assurance Framework for Healthcare

no code implementations • 4 Aug 2022 • Mark Sendak, Gaurav Sirdeshmukh, Timothy Ochoa, Hayley Premo, Linda Tang, Kira Niederhoffer, Sarah Reed, Kaivalya Deshpande, Emily Sterrett, Melissa Bauer, Laurie Snyder, Afreen Shariff, David Whellan, Jeffrey Riggio, David Gaieski, Kristin Corey, Megan Richards, Michael Gao, Marshall Nichols, Bradley Heintze, William Knechtle, William Ratliff, Suresh Balu

The approaches by which the machine learning and clinical research communities utilize real world data (RWD), including data captured in the electronic health record (EHR), vary dramatically.

"The Human Body is a Black Box": Supporting Clinical Decision-Making with Deep Learning

no code implementations • 19 Nov 2019 • Mark Sendak, Madeleine Elish, Michael Gao, Joseph Futoma, William Ratliff, Marshall Nichols, Armando Bedoya, Suresh Balu, Cara O'Brien

Our work underscores the limits of model interpretability as a solution to ensure transparency, accuracy, and accountability in practice.

BIG-bench Machine Learning Decision Making +1