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AI is no longer a futuristic concept in healthcare. It’s here, and it’s scaling rapidly.
At RSNA last year and ECR a few weeks ago, the conversation wasn’t about whether AI works, but how to optimize and scale it. With over 900 FDA-approved AI applications on the market, and hospitals increasingly developing their own in-house models, the challenge is shifting from adoption to integration.
The real question now is: How do we make AI work efficiently and effectively across entire healthcare systems?
We believe a big part of the answer lies in one crucial component: Interoperability.
AI Adoption is Expanding, But So Are the Challenges
Forward thinking healthcare organizations are moving beyond evaluating and deploying one or two clinical AI applications and are now thinking about Enterprise-wide AI strategies, integrating multiple solutions across radiology, cardiology, neurology, pathology and other clinical and operational areas. But with each additional AI application comes complexity, different AI system requirements, different data sources, and different workflows.
Without a structured, big-picture approach to integration, hospitals risk ending up with a complex web of AI solutions that can strain already over-burdened hospital IT teams and slow down clinical workflows rather than enhancing them.
This is where interoperability comes in.
So why is Interoperability such a key consideration for healthcare AI adoption?
1. Simplifies Complexity & Reduces IT Burden
IT teams already manage countless systems, regulatory requirements, and security protocols while managing an increasing number of AI applications.
A centralized, platform-based integration model streamlines AI adoption, minimizing the need for multiple direct connections and ensuring that current and future AI solutions can be added without overwhelming staff.
2. Enhances AI orchestration with Non-Imaging Clinical Data
AI orchestration works best when imaging data is combined with non-imaging clinical data. Integrating other types of information such as patient history, lab results, and other types of clinical information can significantly improve clinicians’ AI experience.
3. Seamless Workflow Integration
AI insights need to be embedded directly into clinical workflows - whether that’s the radiology PACS, reporting systems or the hospitals’ EHR. Interoperability ensures that AI outputs reach the right people at the right time without added complexity.
Standardization: The Backbone of AI Interoperability
Healthcare has several important data standards, from DICOM for medical imaging to HL-7 and FHIR for enterprise messaging and systems integration. A well-designed Enterprise AI Platform must be able to easily integrate with multiple data sources and formats, ensuring AI insights can be readily accessed and sharing AI outputs share across related systems.
While FHIR adoption continues to grow, health systems may still have various needs for data integration with HL7 v2.x or hybrid integrations across clinical systems. That’s why AI platforms need a versatile approach to interoperability, one that supports multiple standards and makes integration as seamless as possible.
Beyond Radiology: The Future of Enterprise AI
AI adoption is rapidly expanding across radiology, but also into other clinical domains such as cardiology, ophthalmology, pathology, and other specialties. This shift means hospitals need an AI strategy that works across departments, not just in silos.
A strong interoperability framework ensures that hospitals can easily scale their investment in AI as demands grow into other clinical domains and into broader clinical decision-making. Whether it’s AI-driven triage in the ER, cardiology risk assessments, or precision oncology, AI’s full potential can only be realized if it works seamlessly across the Healthcare Enterprise.
The Path Forward
A successful healthcare AI strategy isn’t just about having the best algorithm, it’s about ensuring AI works within and across the broader healthcare ecosystem. A big part of successful Enterprise AI deployment depends on:
- Interoperability-first approaches that simplify integration.
- Seamless data exchange between AI systems and clinical workflows.
- Scalability across multiple specialties, not just radiology.
At Blackford, we’re committed to helping healthcare organizations scale AI efficiently, securely, and impactfully. By focusing on interoperability, we can ensure that AI doesn’t just exist in healthcare but practically drives value for both providers and patients.