In the past decade, the application of machine learning (ML) to healthcare has helped drive
the automation of physician tasks as well as enhancements in clinical capabilities and …

Artificial intelligence systems for health care, like any other medical device, have the
potential to fail. However, specific qualities of artificial intelligence systems, such as the …

Interest in machine-learning applications within medicine has been growing, but few studies
have progressed to deployment in patient care. We present a framework, context and …

Algorithmic transparency entails exposing system properties to various stakeholders for
purposes that include understanding, improving, and contesting predictions. Until now, most …

Influence functions efficiently estimate the effect of removing a single training data point on a
model's learned parameters. While influence estimates align well with leave-one-out …

Abstract Machine learning-based clinical decision support tools for sepsis create
opportunities to identify at-risk patients and initiate treatments at early time points, which is …

Influence functions approximate the effect of training samples in test-time predictions and
have a wide variety of applications in machine learning interpretability and uncertainty …

Background It has been rightfully emphasized that the use of AI for clinical decision making
could amplify health disparities. An algorithm may encode protected characteristics, and …

Removing information from a machine learning model is a non-trivial task that requires to
partially revert the training process. This task is unavoidable when sensitive data, such as …

Important decisions that impact humans lives, livelihoods, and the natural environment are
increasingly being automated. Delegating tasks to so-called automated decision-making …