In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …

Machine learning models are increasingly adopted for facilitating clinical decision-making.
However, recent research has shown that machine learning techniques may result in …

A multitude of work has shown that machine learning-based medical diagnosis systems can
be biased against certain subgroups of people. This has motivated a growing number of …

Benefiting from the digitization of healthcare data and the development of computing power,
machine learning methods are increasingly used in the healthcare domain. Fairness …

To study mental illness and health, in the past researchers have often broken down their
complexity into individual subsystems (eg, genomics, transcriptomics, proteomics, clinical …

Modern predictive models require large amounts of data for training and evaluation,
absence of which may result in models that are specific to certain locations, populations in …

AI systems are quickly being adopted in radiology and, in general, in healthcare. A myriad of
systems is being proposed and developed on a daily basis for high-stake decisions that can …

Medical image analysis is a vibrant research area that offers doctors and medical
practitioners invaluable insight and the ability to accurately diagnose and monitor disease …

Mitigating algorithmic bias is a critical task in the development and deployment of machine
learning models. While several toolkits exist to aid machine learning practitioners in …

While many deep-learning-based computer-aided detection systems (CAD) have been
developed and commercialized for abnormality detection in chest radiographs (CXR), their …