His work reflects how AI-native platforms address singular challenges in financial data handling, fraud detection, and risk ...

The patient knowledge gap is considered to be a critical shortcoming for the medical community, particularly at a time when ...

Many past machine learning approaches to microplastic detection have been criticised for relying on idealised datasets ...

As customer expectations evolve, businesses are seeking more advanced AI solutions that can bridge the gap between automated ...

The research shows that informal learning sources, such as social media, are the most common ways students acquire phishing ...

While AI can improve how legal practitioners function and how people access the justice system, Carla L. Reyes, a recognized ...

As technology continues to reshape the insurance and reinsurance industry, companies must adapt by differentiating with AI, navigating capital ...

Gade explains, “for AI to be successfully implemented at scale, people need to learn to rely on similar abstractions, and AI needs to amplify trust. People need to understand how the machine is ...

In my previous article, I discussed the importance of AI explainability and the different categories of AI explainability, explainable predictions, explainable algorithms and interpretable ...

It is important that organizations understand who trains their AI systems, what data was used and, just as importantly, what went into their algorithms’ recommendations. A high-quality explainable AI ...

A novel image-based deep learning approach achieves high accuracy and interpretability, offering potential for clinical ...