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While AI has become a common topic in healthcare, adoption in radiology continues to face deep challenges. The Radiology AI Summit 2025, co-hosted by the London Institute for Healthcare Engineering and deepc, reminded us that the journey from research to real-world clinical impact is still at an early stage in many ways.
Radiology AI is often seen as one of the most advanced areas in medical AI, yet most hospitals have not adopted it at scale. According to the Royal College of Radiologists, more than half of UK radiology departments use some form of AI for medical imaging, but meaningful integration remains limited and financial return unclear.
Dr Katharine Halliday, President of the RCR, described the pressure clearly. There is a 30% shortage of radiologists, rising imaging demand, and growing patient backlogs. She stated, “We need to do something completely different,” pointing to AI as the most realistic option to help address the gap.
But introducing AI into clinical practice is not just a technology issue. It is also a challenge related to operations, regulation, trust, and economics.
Why Radiology AI Fails Without Workflow Integration
Hospitals are held back by underfunded IT teams, siloed data, and limited interoperability. Radiologists still move between PACS, RIS, EHRs, and other systems. Each additional step slows progress and adds friction, making it especially difficult to deploy AI solutions that require yet another layer of coordination and system alignment.
This is where deepcOS® comes in as the infrastructure layer that allows AI to function as part of the clinical workflow. deepcOS® integrates directly into existing hospital infrastructure, requiring a major IT overhaul. Through its secure Gate and API-based architecture, hospitals can deploy and monitor AI tools in a centralized but flexible way, streamlining setup and minimizing additional IT workload. This infrastructure-first approach reduces delays and embeds AI directly into the radiologist’s daily workflow.
By embedding AI into the day-to-day workflow through its infrastructure layer, deepcOS® enables automation, reduces delays, and supports radiologists without disrupting how they work.
The Real-World Evidence Challenge in Radiology AI
The Summit emphasized a growing priority across both public health systems and private providers: real-world evidence. And as Professor Sebastien Ourselin noted, research results aren’t enough. To justify long-term investment, decision-makers need proof that AI improves clinical outcomes, operational efficiency, or financial performance.
But traditional evaluation processes aren’t built for scale. Most hospitals lack the tooling to test AI in their own environments, collect feedback, and measure performance over time.
deepcOS® is designed for exactly this challenge. It provides a structured, vendor-neutral evaluation framework with silent mode testing, usage tracking, and audit logging. Hospitals can test multiple AI solutions on the same datasets and collect both quantitative metrics and user-level feedback. This transforms AI evaluation into a repeatable, data-driven process—one that not only supports internal decision-making but also strengthens procurement cases and regulatory documentation.
Building Clinician Trust in AI Through Usability and Education
The issue is not that clinicians do not believe in AI. Many are too stretched to lead the rollout. Many radiologists are open to new tools, but they need support from hospital leadership. Guidelines like those from the RCR are helpful, but adoption takes time and coordination.
For AI to work, it must be practically invisible, eliminating the need for context switching. This is exactly what deepcOS® is designed to do. By routing AI results directly into PACS, viewers, or structured reports, deepcOS® ensures radiologists interact with AI in the flow of care. There is no need to leave familiar systems. AI becomes part of the diagnostic process—not an additional burden.
How to Enable Scalable Radiology AI Adoption
Radiology AI cannot scale on a foundation of bespoke pilots and point integrations. If AI is to become a standard part of radiology, it must be treated as core infrastructure. That means having clear funding models, shared national datasets, ongoing monitoring, and strong business cases. It also means giving radiologists more time to focus on expert decisions, not system management.
That requires an infrastructure-ready platform that can support this level of complexity.
deepcOS® supports multi-site deployments, elastic cloud scaling, and flexible integration into heterogeneous IT environments. This ensures that as imaging volumes rise and use cases expand, hospitals can grow their AI footprint without starting from scratch.
Events like RAIS are helping shift the conversation toward these practical realities. But platforms like deepcOS® are turning those conversations into action.
As Dr Halliday said, “AI can help us meet the challenges of the future. In fact, it is our only hope. But we need to embrace it and lead the change.”
The technology is ready. The evidence is growing. What remains is the leadership to bring it into everyday care.
Frequently Asked Questions
What is holding back AI in radiology?
AI in radiology faces several barriers, including a lack of workflow integration, limited funding, unclear financial return, and data infrastructure gaps. Clinicians also need more time and support to adopt new tools.
How do hospitals evaluate radiology AI tools?
Hospitals assess AI tools using real-world testing, performance metrics, and usability feedback. deepcOS® offers silent mode testing and usage monitoring to support this process.
How can AI be integrated into existing radiology workflows?
AI tools should work within the existing hospital systems. Integration should not require major IT overhauls. Platforms like deepcOS® connect AI tools to PACS, RIS, and EHR systems to ensure a smooth experience.
Why is real-world evidence important for AI in radiology?
Real-world evidence shows how AI performs in daily clinical practice. It helps decision-makers understand the impact of AI on patient care and operations, making it easier to justify long-term investment.
What are some benefits of radiology workflow automation?
Automation in radiology can reduce reporting times, minimize manual errors, and improve throughput. AI tools that integrate seamlessly into workflows help radiologists focus more on interpretation and less on administrative tasks.
Explore how deepcOS® is helping radiology departments move forward: Book a Meeting
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