Considerations in the reliability and fairness audits of predictive models for advance care planning

Considerations in the reliability and fairness audits of predictive models for advance care planning

Multiple reporting guidelines for artificial intelligence (AI) models in healthcare recommend that models be audited for reliability and fairness. However, there is a gap of operational guidance for performing reliability and fairness audits in practice. Following guideline recommendations, we condu...

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Bibliographic Details
Main Authors: Jonathan Lu, Amelia Sattler, Samantha Wang, Ali Raza Khaki, Alison Callahan, Scott Fleming, Rebecca Fong, Benjamin Ehlert, Ron C. Li, Lisa Shieh, Kavitha Ramchandran, Michael F. Gensheimer, Sarah Chobot, Stephen Pfohl, Siyun Li, Kenny Shum, Nitin Parikh, Priya Desai, Briththa Seevaratnam, Melanie Hanson, Margaret Smith, Yizhe Xu, Arjun Gokhale, Steven Lin, Michael A. Pfeffer, Winifred Teuteberg, Nigam H. Shah
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-09-01
Series:Frontiers in Digital Health
Subjects:
model reporting guideline
electronic health record
artificial intelligence
advance care planning
fairness
audit
Online Access:https://www.frontiersin.org/articles/10.3389/fdgth.2022.943768/full

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https://www.frontiersin.org/articles/10.3389/fdgth.2022.943768/full

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