Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to
a readily accessible tool for cancer researchers. AI-based tools can boost research …

This paper discusses some overlooked challenges faced when working with machine
learning models for histopathology and presents a novel opportunity to support “Learning …

Background: Lack of trust in artificial intelligence (AI) models in medicine is still the key
blockage for the use of AI in clinical decision support systems (CDSS). Although AI models …

The growing field of explainable Artificial Intelligence (xAI) has given rise to a multitude of
techniques and methodologies, yet this expansion has created a growing gap between …

The adoption of advanced artificial intelligence (AI) systems in healthcare is transforming the
healthcare-delivery landscape. Artificial intelligence may enhance patient safety and …

Abstract Background and Objective In Pancreatic Ductal Adenocarcinoma (PDA), multi-omic
models are emerging to answer unmet clinical needs to derive novel quantitative prognostic …

Integrating large language models (LLMs) to retrieve targeted medical knowledge from
electronic health records enables significant advancements in medical research. However …

Introduction Glioblastoma (GBM) is a highly aggressive malignant tumor of the central
nervous system that displays varying molecular and morphological profiles, leading to …

The advent of digital pathology and the deployment of high‐throughput molecular
techniques are generating an unprecedented mass of data. Thanks to advances in …

Traditional histopathology, characterized by manual quantifications and assessments, faces
challenges such as low-throughput and inter-observer variability that hinder the introduction …