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, ...

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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 ...

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 ...

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 ...

The black box conundrum is one of the biggest roadblocks that can prevent banks from executing their AI strategies. Machine learning (ML) models tasked with identifying patterns in data, making ...

While machine learning and deep learning models often produce good classifications and predictions, they are almost never perfect. Models almost always have some percentage of false positive and false ...

AI systems have tremendous potential, but the average user has little visibility and knowledge on how the machines make their decisions. AI explainability can build trust and further push the ...

While the idea of a black box is intriguing, it can miss the point. Placing information into a black box that spits out a solution is valuable but understanding what’s going on in that black box is ...

What is explainable AI (XAI)? What are some of the use cases for XAI? What are the technology requirements for implementing XAI? Anomaly detection is the process of identifying when something deviates ...