Patient and Caregiver Perceptions of an Interface Design to Communicate Artificial Intelligence–Based Prognosis for Patients With Advanced Solid Tumors Consecutive inpatients who underwent ...

In past roles, I’ve spent countless hours trying to understand why state-of-the-art models produced subpar outputs. The underlying issue here is that machine learning models don’t “think” like humans ...

Using Real-World Data for Machine-Learning Algorithms to Predict the Treatment Response in Advanced Melanoma: A Pilot Study for Personalizing Cancer Care This study aims to investigate the impact of ...

Artificial intelligence (AI) and machine learning (ML) are revolutionizing the way we understand and predict soil processes. Yet, while data-driven models ...

How AI, privacy-preserving computation, and explainable models quietly strengthen payments, protect data, and bridge traditional finance with crypto systems.

According to a new study, machine learning can reliably identify patients at high risk of early dysphagia following acute ...

This course explores the field of Explainable AI (XAI), focusing on techniques to make complex machine learning models more transparent and interpretable. Students will learn about the need for XAI, ...

In data analysis, time series forecasting relies on various machine learning algorithms, each with its own strengths. However, we will talk about two of the most used ones. Long Short-Term Memory ...

This issue of The Journal of Risk Model Validation features two papers that directly address validation using machine learning. Whether their findings imply we will all (including the editor) become ...

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...