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 ...

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 ...

NetraAI’s biological and clinical signatures successfully boosted the accuracy of all eight evaluated algorithms across every single dataset ...

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 ...

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, ...

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 ...

Protein language models are artificial intelligence tools which help engineer proteins with useful properties, including completely new structures never seen before in nature. The technology has huge ...

SRINAGAR: A new study of sediments from Wular Lake has identified three principal geological sources feeding the Kashmir lake ...