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, ...
A systematic review of 237 studies from 2014 to 2024 maps how explainable AI techniques such as SHAP and LIME are making ...
Automated machine learning has long promised to hand the power of deep learning to scientists who never trained as programmers, yet most of these tools deliver a finished model with little explanation ...
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 ...
Are deep learning models truly untrustworthy? Or are we simply governed by the naive intuition that 'what cannot be explained ...
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 ...
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 ...
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, ...
In the realm of Intensive Outpatient Programs (IOP), machine learning is reshaping how facilities such as those in Scottsdale operate. By integrating advanced ...
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