A systematic review of 118 Web of Science publications on artificial intelligence sovereignty in health, mapping data, digital, technological, epistemic and Indigenous data sovereignty…

The EU AI Act and healthcare: prohibited practices, high-risk AI systems, hidden risks and the regulatory paths that are still uncharted.

Principles for understanding trust in AI from Everett et al. (2026), Nature Reviews Psychology: a conceptual map and implications for healthcare.

AI ethics beyond principles: how ethical concerns are framed across principles, lived realities and power structures.

Who is liable when AI harms a patient? A new methodology for analysing medical liability in AI-assisted clinical decisions.

Why explainable AI alone does not build clinical trust: the human factors that shape how clinicians use and rely on AI recommendations.

One shot at trust: how to build credible evidence for medical artificial intelligence before clinical deployment.

Tani et al. bibliometric review of 102 papers on AI ethics in management (2016 onward): three time slices, motor themes, generative AI and a…

Digital determinants of health: how data poverty creates bias in AI and machine learning for healthcare.

How AI software as a medical device (AI-SaMD) is regulated around the world, and the case for harmonised global regulation.

How artificial intelligence policy frameworks differ across China, the EU and the US, and what that means for governance.

The argument that regulating ‘AI’ as a category is flawed, and why rules should target uses and harms instead.

When code isn’t law: rethinking regulation for artificial intelligence and the limits of rule-based approaches.

Why risk-based AI regulation such as the EU AI Act falls short, and what a structuration theory approach suggests for AI governance.

How artificial intelligence is applied in drug regulation: evidence review, safety monitoring and regulatory science.

The Swiss cheese model applied to AI safety: a taxonomy of layered defences against AI failures.

A scoping review of fair machine learning in real-world healthcare data: methods, metrics and gaps.

A scoping review of AI fairness in healthcare: types of bias, fairness metrics and mitigation methods.

Empirically derived evaluation protocols for AI in healthcare: what is needed to ensure safety.