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 …

Background Artificial intelligence (AI) research in healthcare is accelerating rapidly, with
potential applications being demonstrated across various domains of medicine. However …

There is tremendous enthusiasm surrounding the potential for machine learning to improve
medical prognosis and diagnosis. However, there are risks to translating a machine learning …

Background Despite the expanding use of machine learning (ML) in fields such as finance
and marketing, its application in the daily practice of clinical medicine is almost non-existent …

Abstract Machine learning can help clinicians to make individualized patient predictions only
if researchers demonstrate models that contribute novel insights, rather than learning the …

Artificial intelligence (AI) has been the subject of considerable interest for many years for its
potential to improve clinical care—yet its actual impact on patient outcomes when deployed …

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

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 …

Traditional healthcare systems have grappled with meeting the diverse needs of millions of
patients, resulting in inefficiencies and suboptimal outcomes. However, the emergence of …

Objective We aimed to investigate bias in applying machine learning to predict real-world
individual treatment effects. Materials and Methods Using a virtual patient cohort, we …