• Evaluation of machine-learned systems is a multifaceted process that encompasses
internal validation, clinical validation, clinical outcomes evaluation, implementation research …

• Multidisciplinary partnership between technical experts and end-users, including clinicians,
administrators, and patients and their families, is essential to developing and implementing …

Abstract Machine learning methods, a subdomain of artificial intelligence, in healthcare have
been experiencing rapid growth and development but these methods have also been …

Examples of fully integrated machine learning models that drive clinical care are rare.
Despite major advances in the development of methodologies that outperform clinical …

Abstract Machine learning algorithms are being used to screen and diagnose disease,
prognosticate and predict therapeutic responses. Hundreds of new algorithms are being …

Several sources of bias can affect the performance of machine learning systems used in
medicine and potentially impact clinical care. Here, we discuss solutions to mitigate bias …

Despite enormous enthusiasm, machine learning models are rarely translated into clinical
care and there is minimal evidence of clinical or economic impact. New conference venues …

There is little known about how academic medical centers (AMCs) in the US develop,
implement, and maintain predictive modeling and machine learning (PM and ML) models …

As machine learning (ML) shows vast potential in real world applications, the number of
deployed models has been increasing substantially, but little attention has been devoted to …

Abstract Machine learning (ML) has the potential to transform patient care and outcomes.
However, there are important differences between measuring the performance of ML models …