When machine learning models deliver problematic results, it can often happen in ways that humans can't make sense of, and this becomes dangerous when there are no limitations of the model, ...

Distributional regression (DR) refers to regression methods that model the entire conditional probability distribution of a response variable given a set of explanatory variables. The generalized ...

As artificial intelligence usage continues to increase, there’s a problem lurking in the background growing larger by the day: It’s the ability of AI to explain itself so it’s clear what led to an ...

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 ...

Creating machine learning models that generate accurate results is one thing, but it's quite another to ensure model interpretability -- the ability to understand why the ML models that power AI tools ...

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 ...

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, ...

Every radiologist knows the frustration: the AI model that works brilliantly at one hospital can stumble badly at another.

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 ...

Image courtesy by QUE.com The Paradigm Shift in Machine Learning Architectures As we move into 2026, the landscape of Machine Learning ...