The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI)
models is likely to usher in newfound capabilities in medicine. We propose a new paradigm …

In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

Data drift refers to differences between the data used in training a machine learning (ML)
model and that applied to the model in real-world operation. Medical ML systems can be …

Artificial intelligence and machine learning are driving a paradigm shift in medicine,
promising data-driven, personalized solutions for managing diabetes and the excess …

Clinical prediction models must be appropriately validated before they can be used. While
validation studies are sometimes carefully designed to match an intended population/setting …

In the current development and deployment of many artificial intelligence (AI) systems in
healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent …

Temporal distribution shift negatively impacts the performance of clinical prediction models
over time. Pretraining foundation models using self-supervised learning on electronic health …

Increasingly complex learning methods such as boosting, bagging and deep learning have
made ML models more accurate, but harder to interpret and explain, culminating in black …

Temporal dataset shift associated with changes in healthcare over time is a barrier to
deploying machine learning-based clinical decision support systems. Algorithms that learn …

While there have been a very large number of academic studies of proposed machine
learning-based health predictive models, it is widely recognized that machine learning …