AI-driven Orchestration is Helping Transform Medical Imaging

For the past decade, artificial intelligence's (AI) potential in healthcare has been synonymous with speed. In medical imaging, the logic was straightforward: If technology could analyze images faster than the human eye, cases could be triaged sooner. If software could pre-populate reports, short-staffed radiology teams could move through their worklists faster. And to a large extent, AI has delivered. Early deployments successfully accelerated diagnoses and reduced report turnaround times, alleviating the pressure on imaging departments operating at capacity.

However, health systems with advanced AI tool adoption may be reaching the point of diminishing returns. It’s becoming clear that making individual functions faster doesn’t automatically make entire systems more efficient. In fragmented environments, speeding up one step can simply create bottlenecks elsewhere.

The industry is now less interested in success metrics that prioritize an algorithm’s speed, choosing instead to evaluate how well it coordinates with the rest of the ecosystem. Maximizing AI’s orchestration potential requires the connection of disparate systems, specialties and data silos rather than more solutions that expedite tasks and offer point improvements.

Fragmentation Limitations

The current state of imaging infrastructure is a solid indication that orchestration should be the industry’s next important step. Many organizations rely on IT landscapes supported by legacy system patchworks. Radiology may operate on one PACS, for example, while cardiology uses another, and the EHR sitting above them struggles to pull clinical data from these silos.

Fragmented environments can isolate AI tools and limit their usefulness. An algorithm that detects a serious abnormality or condition is of little help if its findings are trapped in an application that doesn’t communicate well with a radiologist’s viewer. Any productivity gains are lost to swivel-chair interoperability, which forces clinicians to toggle between screens, log into separate portals and manually bridge the gaps between systems.

This friction is costly to clinicians and patients. Despite the availability of countless efficiency tools, burnout is rampant — with 54% of radiologists reporting burnout symptoms, likely driven by administrative burdens and disjointed workflows. Without seamless integration, AI point solutions are just another alert to manage or another window to open, which takes important time away from patient care.

The Connective Tissue of Enterprise Imaging

Medical imaging’s utilization of AI started with detection but has progressed to the orchestration phase. In healthcare, orchestration encompasses the automated arrangement, coordination, and management of systems and services. In the context of imaging, though, it acts as an intelligent organizational control layer. It sits between image storage platforms and the reading process, ensuring that the right study reaches the right specialist at the right time, regardless of where the image was acquired. The shift from speed tool to orchestration platform changes the value of AI in several important ways:

1. Data normalization and intelligent routing: One of the biggest challenges in imaging is that different systems have different ways of communicating. A hospital ER system might code a chest X-ray differently than its outpatient clinic, for instance. Orchestration solves this by standardizing how studies and patient data are presented to radiologists, even when underlying PACS and EHR systems differ. Normalized and complete data facilitate the orderly flow of studies into a unified worklist, eliminating variability and manual workarounds. Next, intelligent routing enables systems to assign cases based on urgency, expertise and current workload.

2. Contextual alignment: Orchestration eliminates the inconvenience of navigating multiple applications with support from standards-based integration. When imaging appears within the same clinical interface used for other patient data — such as lab results, notes, and orders — users spend less time logging into separate systems and re-entering patient data. This cuts down on the friction experienced throughout a radiologist’s shift, saves valuable time, and reduces the risk of error.

3. Bridging the gap between specialties: Imaging is typically thought of as radiology-centric. However, enterprise-wide orchestration supports the understanding that imaging is vital to other specialties, such as cardiology, orthopedics, and oncology. Cardiology, in particular, often operates in a digital silo, with separate reporting structures and archives. An orchestrated approach breaks down walls, enabling a vascular surgeon, for example, to view a cardiology echo and a radiology CT side-by-side in a unified viewer without launching separate applications. This interoperability fosters better collaboration between departments and offers providers a more holistic view of their patients’ health.

Standards and Governance are Critical to AI Orchestration

While software makes the transition to an orchestrated, interoperable model possible, a commitment to standards and governance make it sustainable. Interoperability frameworks like Integrating the Healthcare Enterprise (IHE), HL7 CDA, and DICOM web standards serve as rules that allow different vendors to seamlessly exchange information. Health systems must choose vendors who build on these open standards to ensure modular access and improvements. Being able to swap out one algorithm for an updated one in three years without disassembling large swaths of infrastructure is invaluable.

To cement AI’s place as a core operational engine, organizations need multidisciplinary oversight teams composed of radiologists, IT leaders, data scientists and administrators. This group should be responsible for establishing and upholding the rules of engagement for orchestration:

• Validation: Verify that AI solutions are working as intended, such as accurately normalizing data and routing cases correctly.

• Transparency and explainability: Ensure it’s clear to the clinician why a study was prioritized or assigned and how the information behind the prioritization was assessed.

• Fail-safes: Determine what will happen if the orchestration layer goes down.

The "human in the loop" remains the ultimate authority. Orchestration should serve the clinician, presenting them with the information they need to make a decision, but never making the clinical decision for them.

Moving Toward a Connected Ecosystem

The initial excitement around AI in healthcare was driven by the capability to do things faster. Looking ahead, though, the most successful organizations will be those that use the technology to do things together. Shifting from fragmented point solutions to smooth orchestration is the most effective way to scale imaging services and meet rising demand for care — without overloading the workforce that provides it.

Healthcare leaders who prioritize interoperability, data normalization, and unified workflow management can help keep the digital transformation on track. When done correctly, technology should fade into the background and free clinicians to focus entirely on their patients.

Jordan Bazinsky is the CEO of Intelerad. He most recently served as executive vice president at Cotiviti. Jordan earned an MBA from Harvard University and a bachelor’s degree from Duke University, where he previously served on the Board of Trustees.