Imaging Intelligence in Practice

Imaging is entering a more demanding chapter of digital transformation. AI is no longer a distant promise or a standalone pilot sitting at the edge of the workflow. It is increasingly expected to function like any other clinical capability: dependable, auditable and aligned with patient outcomes, service performance and professional accountability. That shift changes the questions leaders need to ask. Instead of “Does it work?”, the focus becomes “Where does it fit, who is responsible, what does it change, and how will we know it is helping?”

In practice, the difference between progress and frustration often comes down to the basics done well. Governance must be more than a policy statement, with clear ownership, clinical oversight and a route from evaluation to adoption. Integration decisions matter because tools that sit outside reporting, scheduling and quality processes rarely deliver sustained value. Measurement matters because benefits that cannot be demonstrated, monitored and compared over time are difficult to defend, fund or scale. And as AI becomes more deeply embedded, organisations need to treat ongoing performance, drift, bias and safety monitoring as routine operational work rather than exceptional tasks.

This issue examines how imaging organisations can translate AI ambition into safe, measurable and sustainable practice, with a focus on governance, infrastructure thinking, ROI discipline, expanding access and practical implementation frameworks. In the articles below, contributors consider what it takes to embed intelligence into everyday imaging while strengthening accountability, oversight and long-term value.

Mathias Goyen explains how responsible AI adoption in radiology depends on governance, clinician oversight and measurable clinical value.

James Thannickal contends that radiology should treat AI as core infrastructure to align integration, governance and measurement.

Harvey Castro and Soner Haci show how AI-guided ultrasound can decentralise imaging and extend diagnostic access.

Dr. Quoc Duy Vo clarifies how radiology AI ROI depends on defined aims, full cost capture and workflow integration.

Dr. Casmir Otubo maintains that imaging AI must be governed as clinical infrastructure, requiring continuous monitoring, drift detection and structured post-deployment oversight.

Debarati Sengupta traces AI’s shift from detection to prediction across imaging, highlighting workflow gains and the governance, interoperability and bias challenges.

Yuri Vasilev et al. outline how Moscow has embedded radiology AI through URIS-enabled infrastructure, multi-stage clinical validation, standardisation and the MosMedAI platform’s nationwide rollout.

Penggalih M. Herlambang maps how GenAI can build low-cost simulators and calculators, while requiring validation and data security.

Isabel Page emphasises that safeguarding in youth sport requires system-level governance, independent oversight and clear accountability for long-term risk.

Christian Marolt reviews on Prof. Goyen’s reflections on leadership, resilience and the unseen lessons shaping medical careers.

Susana Álvarez Gómez sets out a practical framework for implementing value-based procurement of medical devices in healthcare systems.

I hope you enjoy this issue and take away practical ideas you can apply immediately, whether you are setting strategy, building governance, evaluating solutions or supporting teams through adoption.

Happy reading!