Integrating Imaging Tools and AI in Radiology Depends on Standards-Based Interoperability

Integrating Imaging Tools Helps Radiology AI Deliver Real Value

Interoperability often works behind the scenes in health care, yet it's essential for efficiency and better patient outcomes. It allows different systems to exchange and use data seamlessly. Without it, there would be no sharing of electronic health records (EHRs), no labs sending results to hospitals electronically, and no prescriptions sent directly to pharmacies.

Interoperability simplifies processes for both vendors and users. For example, it changes manual reporting of measurements or AI results into automated population within radiology reports, saving time and reducing errors.

Standards Are the Foundation

Standards are the agreed-upon rules and guidelines that make interoperability possible. In radiology, two key standards are DICOM (Digital Imaging and Communications in Medicine) and HL7 (Health Level 7). These enable storing, transmitting, and viewing medical images and related data across different devices and systems.

However, simply having standards isn’t enough. Different vendors often apply these standards in varying ways, causing incompatibility between their systems. This requires an extra step: integrating these standards so that products from different companies can work together smoothly.

The Role of IHE in Integration

The Integrating the Healthcare Enterprise (IHE) initiative provides guidance on how to apply existing standards like DICOM and HL7 to specific healthcare use cases.

• IHE creates “profiles” — detailed scripts that developers follow to build interoperable solutions.
• Following these profiles ensures compatibility across systems, such as electronic medical records and imaging software.
• IHE organizes Connectathons, events where vendors test and refine their products to meet these profile standards.

For product developers, these profiles serve as clear roadmaps for building systems that fit into multivendor environments without friction.

AI Adds New Layers to Imaging Workflow

AI promises to automate many tasks in radiology, but it also introduces new interoperability challenges. Adding AI software means adding new systems that must communicate effectively with existing workflows.

Fortunately, AI integration doesn't require reinventing the wheel. Existing standards like DICOM and HL7 remain the key tools for integrating AI into imaging workflows.

The main challenge lies in getting AI vendors, many new to medical technology, to properly adopt these standards. When AI products ignore established protocols, radiologists face difficulties in monitoring AI outputs, integrating results into reports, and maintaining smooth workflows. This disruption undermines the value AI can bring.

Driving Adoption Through Demand

Healthcare organizations and radiologists must insist that AI vendors support IHE profiles. One effective approach is including these requirements in requests for proposals (RFPs).

For example, if a system needs to support radiation monitoring in a standardized way, the RFP should specify compliance with relevant IHE profiles. This ensures vendors build solutions that integrate seamlessly.

Radiologists play a vital role by providing feedback on AI solutions during development and deployment. This input helps refine IHE profiles and improve interoperability standards over time.

From Why to How

Everyone agrees on the need for interoperability, but the real work is figuring out how to achieve it as AI becomes part of the radiology workflow.

Success depends on strong leadership, organizational support, and active collaboration among radiologists, clinicians, and vendors. When these elements align, integrating AI into imaging workflows will become a practical reality that benefits all stakeholders.

For product developers interested in expanding their AI expertise in healthcare, exploring specialized AI courses by job role can provide valuable insights into standards, workflows, and practical implementation.