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

The guidance allows for different levels of transparency depending on whether artificial intelligence is used in direct ...

de Filippis, R. and Al Foysal, A. (2026) Cross-Population Transfer Learning for Antidepressant Treatment Response Prediction: A SHAP-Based Explainability Approach Using Synthetic Multi-Ethnic Data.

Artificial intelligence is seeing a massive amount of interest in healthcare, with scores of hospitals and health systems already have deployed the technology – more often than not on the ...

Would you blindly trust AI to make important decisions with personal, financial, safety, or security ramifications? Like most people, the answer is probably no, and instead, you’d want to know how it ...

Forbes contributors publish independent expert analyses and insights. James Broughel is an economist focused on the economics of regulation. Just because aspects of artificial intelligence systems are ...

As enterprises shift from AI experimentation to scaled implementation, one principle will separate hype from impact: explainability. This evolution requires implementing 'responsible AI' frameworks ...

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 ...

Two of the biggest questions associated with AI are “why does AI do what it does”? and “how does it do it?” Depending on the context in which the AI algorithm is used, those questions can be mere ...