Explainable Artificial Intelligence (XAI) seeks to render the operation and decisions of complex machine learning systems transparent and interpretable to users, regulators and other stakeholders. As ...

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 a real-world, nationwide electronic health record–derived deidentified database of 38,048 patients with advanced NSCLC, we trained binary prediction algorithms to predict likelihood of 12-month ...

Lung cancer (LC) is a leading cause of cancer-related mortality in the United States. Accurate prediction of LC mortality rates is crucial for guiding targeted interventions and addressing health ...

Across the UK, financial institutions are using machine learning models to make decisions that affect millions of people. These decisions include credit approvals, fraud alerts, investment ...

Scientists have developed and tested a deep-learning model that could support clinicians by providing accurate results and clear, explainable insights—including a model-estimated probability score for ...

Researchers at Indiana University School of Medicine have developed and tested a six-point scoring system that can help ...

A one-size-fits-all approach likely isn't the best strategy when designing artificial intelligence systems that assist users in disease diagnosis.

A body of trustworthy AI research addresses interpretability, privacy, and robustness through rule-based modeling, federated learning, and adversarial defense. The studies explore how AI systems can ...

Exercise training is a cornerstone of cardiac rehabilitation (CR) for patients with coronary artery disease (CAD), and ...

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, ...