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Preparing for an Artificial Intelligence–Enabled Future: Patient Perspectives on Engagement and Health Care Professional Training for Adopting Artificial Intelligence Technologies in Health Care Settings

Preparing for an Artificial Intelligence–Enabled Future: Patient Perspectives on Engagement and Health Care Professional Training for Adopting Artificial Intelligence Technologies in Health Care Settings

Balthazar et al [25] contended that even when patients have an in-depth understanding and thoughts on the appropriate use of their personal health information, they may not be able to understand the foundational concepts of machine learning models to make predictions or discern the difference between terms such as privacy and confidentiality.

Tharshini Jeyakumar, Sarah Younus, Melody Zhang, Megan Clare, Rebecca Charow, Inaara Karsan, Azra Dhalla, Dalia Al-Mouaswas, Jillian Scandiffio, Justin Aling, Mohammad Salhia, Nadim Lalani, Scott Overholt, David Wiljer

JMIR AI 2023;2:e40973

Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach

Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach

A study by Bredfeldt et al [20] highlighted that education was associated with increased use of key EHR features for medication list management, an addition to the meaningful use criteria. Similarly, Kraus et al [19] reported that physician adoption with HIS reached 40% in the first month and stabilized at 75% within a year.

David Wiljer, Mohammad Salhia, Elham Dolatabadi, Azra Dhalla, Caitlin Gillan, Dalia Al-Mouaswas, Ethan Jackson, Jacqueline Waldorf, Jane Mattson, Megan Clare, Nadim Lalani, Rebecca Charow, Sarmini Balakumar, Sarah Younus, Tharshini Jeyakumar, Wanda Peteanu, Walter Tavares

JMIR Res Protoc 2021;10(10):e30940