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In 2025, AI is moving from experimentation to implementation. The healthcare industry is no longer just exploring the possibilities — it’s actively embedding AI into workflows to improve clinical outcomes, operational efficiency, and patient experience. But AI is only as good as the data it can access. That’s where AI interoperability becomes a key enabler. This article explores how AI and connected health data are working together to transform care delivery — and what it takes to turn technical potential into real-world impact.
Recent research suggests that generative AI could save the U.S. healthcare system up to $360 billion annually by reducing administrative burden, optimizing clinical documentation, and improving resource allocation. AI-powered chat tools are already helping physicians spend less time on paperwork and more time with patients. In radiology and pathology, AI models — including generative and hybrid approaches — are accelerating image interpretation and detection of anomalies to support clinicians in decision making and help reduce misdiagnosis. These advances highlight the vast potential of AI when integrated thoughtfully into the healthcare workflow. In 2025, healthcare systems in parts of Europe and North America are increasingly moving from pilots to early-stage production deployments. Measurable impact is emerging in areas such as primary care coordination, radiology reporting support, and chronic disease monitoring. Beyond cost savings, AI is now being evaluated for its effect on time to diagnosis, reduced readmissions, and provider satisfaction — marking a shift from experimentation to outcome-driven adoption.
Clinovera, as InterSystems partner, works with IRIS clients to help them leverage the power of the data platform they are on. InterSystems IRIS for Health. IRIS acts as an Integration engine, data platform, analytics platform, a vector database, to name some possibilities, which make for a powerful foundation for AI solutions by enabling real-time data sharing, normalizing diverse formats, and supporting integration across complex health systems. By embedding AI interoperability into this kind of high-performance data environment, healthcare organizations can unlock more accurate, timely, and context-aware insights.
When healthcare systems combine data from different sources, AI can help doctors make better decisions. In a recent pilot program, a hospital group used an AI-enabled platform built on top of IRIS to provide treatment suggestions based on a patient's full medical history. The result was more accurate diagnoses and faster time to treatment, especially for people with multiple chronic conditions.
In emerging pilots, national triage systems are starting to integrate AI to assess patient-reported symptoms and suggest appropriate care levels. Early results indicate a reduction in unnecessary ER referrals and improved triage consistency across age groups, though broader validation is ongoing.
In a Central European health network, combining data from wearable devices with electronic health records made it possible to flag early warning signs of heart problems. This kind of integration allowed care teams to step in sooner and prevent avoidable hospital visits. Over six months, the number of emergency department trips dropped by nearly a third in one primary care network.
Giving GenAI access to health data raises important privacy concerns. Strong data protection measures—including encryption, decentralized learning, and compliance with regulations like GDPR—are essential. Leading technology providers continue to invest in tools that ensure sensitive data stays secure.
Healthcare data often comes in different formats, making it hard for GenAI to analyze consistently. Standards like FHIR are helping, and platforms such as IRIS make it easier to convert and organize information across systems. AI interoperability ensures that these different systems can ‘speak the same language,’ allowing AI models to provide actionable insights despite variations in data structure. Even so, wider industry adoption is still a work in progress.
The Clinovera FHIR Practice is a specialised group of people who work with your team to deliver interoperability. Ensuring your Interoperability strategy is sound and your integrations meet the business needs are some of the areas of focus that we can work with you on. Our engineers are well-trained and can support a myriad of interoperability protocols as you transition towards the FHIR standard.
As AI adoption accelerates in healthcare, organizations are strengthening internal governance frameworks and aligning with evolving external regulations like the EU AI Act and the NIST AI RMF. Validation, clinical oversight, and explainability are now baseline requirements for deploying AI responsibly.
Training programs and clinician-in-the-loop design are essential to ensure that AI tools support—not replace—clinical judgment. Adoption depends on trust, and trust depends on usability, transparency, and alignment with real workflows.
Health systems are conducting AI readiness assessments and building infrastructure for long-term model monitoring, retraining, and compliance. Lifecycle management practices—such as drift detection and performance audits—are becoming standard in regulated environments.
Modern AI solutions are moving beyond isolated use cases. Multi-modal models are increasingly able to integrate EHRs, imaging, and sensor data, supporting a wider range of workflows from diagnosis to patient engagement—unlocking system-wide value.
The right AI tools will further enable your team, reduce burden, and increase your care capabilities. But to succeed at scale, it must be implemented with care, guided by robust data strategies and ethical oversight. Interoperability isn’t just a technical requirement — it’s the foundation for responsible AI in medicine. As systems become more connected, and AI becomes more capable.