With the advent of machine learning (ML) and deep learning (DL) empowered applications
for critical applications like healthcare, the questions about liability, trust, and interpretability …

Recent years have witnessed widespread adoption of machine learning (ML)/deep learning
(DL) techniques due to their superior performance for a variety of healthcare applications …

High prevalence of mental illness and the need for effective mental health care, combined
with recent advances in AI, has led to an increase in explorations of how the field of machine …

Translating machine learning (ML) models effectively to clinical practice requires
establishing clinicians' trust. Explainability, or the ability of an ML model to justify its …

With the advancement in artificial intelligence (AI) and machine learning (ML) techniques,
researchers are striving towards employing these techniques for advancing clinical practice …

Machine learning for healthcare researchers face challenges to progress and reproducibility
due to a lack of standardized processing frameworks for public datasets. We present MIMIC …

There is increasing awareness that the methodology and findings of research should be
transparent. This includes studies using artificial intelligence to develop predictive …

Multiple sclerosis (MS) is a neurological disease of the central nervous system that is the
leading cause of non-traumatic disability in young adults. Clinical laboratory tests and …

Machine and deep learning algorithms are rapidly growing in dynamic research of medical
imaging. Currently, substantial efforts are developed for the enrichment of medical imaging …

When training clinical prediction models from electronic health records (EHRs), a key
concern should be a model's ability to sustain performance over time when deployed, even …