Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the
development of computational programs that can mimic human intelligence. In particular …

Abstract Interest in Machine Learning applications to tackle clinical and biological problems
is increasing. This is driven by promising results reported in many research papers, the …

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 …

Leukemia is a type of cancer affecting blood-forming tissues, where timely diagnosis is
crucial for early intervention and better treatment outcomes. Traditional detection methods …

Identifying disease-causing variants in Rare Disease patients' genome is a challenging
problem. To accomplish this task, we describe a machine learning framework, that we called …

An increasing number of machine learning applications are being developed and applied to
digital pathology, including hematopathology. The goal of these modern computerized tools …

Advanced machine learning (ML) algorithms have outperformed traditional approaches in
various forecasting applications, especially electricity price forecasting (EPF). However, the …

Acute myeloid leukemia (AML) is caused by altered maturation and differentiation of myeloid
blasts, as well as transcriptional/epigenetic alterations, all leading to excessive proliferation …

Objectives AI/ML advancements have been significant, yet their deployment in clinical
practice faces logistical, regulatory, and trust-related challenges. To promote trust and …

Abstract Machine learning (ML) models are increasingly deployed in many critical settings
such as medicine, where ensuring their trustworthiness is a priority. The inherent complexity …