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, ...

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 ...

Artificial intelligence may be able to do more than predict what happens after prostate cancer surgery; it can also help ...

Machine learning accurately distinguished peanut allergy from peanut sensitisation, highlighting its potential to support ...

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 ...

Tech companies have developed machine learning models and algorithms expeditiously in recent years. Those familiar with this technology likely remember a time when, for instance, bank personnel and ...

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 ...

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, ...

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 ...

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