Healthcare is a complex socio-technical system, not a purely technical environment. Clinical decisions are shaped not only by ...

Explainable AI (XAI) exists to close this gap. It is not just a trend or an afterthought; XAI is an essential product capability required for responsibly scaling AI. Without it, AI remains a powerful ...

Most current autonomous driving systems rely on single-agent deep learning models or end-to-end neural networks. While ...

Enterprise AI adoption has entered a more pragmatic phase. For technology leaders, the challenge is no longer convincing the organisation that AI has potential. It is ensuring that systems influencing ...

We study the role of AI transparency and explainability in shaping user trust, comprehension, and decision satisfaction. Our research evaluates how different forms of explanations—such as procedural ...

The key to enterprise-wide AI adoption is trust. Without transparency and explainability, organizations will find it difficult to implement success-driven AI initiatives. Interpretability doesn’t just ...

The clinical trial ecosystem is entering a phase of consolidation and reinvention driven by the collapse of boundaries between functions, data, and even companies themselves.

Beena Wood, Qinecsa, explains how AI could revolutionize pharmacovigilance, if data, trust, skills, and organizational ...

As part of the “Artificial Intelligence Strategy of the Republic of Azerbaijan for 2025–2028,” one of the main objectives is ...

Here's how AI-driven, agentic systems are reshaping software quality engineering, and what organizations can do to prepare ...

The plan is to improve employee productivity, speed up core operations, and introduce new safeguards to ensure transparency ...

AI can help fix that. It can give temporary, task-based access and remove it automatically when the job is done. This is ...