AI has become deeply embedded in clinical medical imaging. Specialized algorithms can now detect intracranial bleeds, measure lung nodules, and summarize clinical documentation into actionable data. The diagnostic capabilities are real, but so is the complexity.
As the number of these AI tools grows, so does the technical overhead required to manage them. Clinicians are often left to manually interpret imaging orders, review patient context, and determine which studies should be sent to which algorithm. These fragmented micro-workflows can introduce delays, increase cognitive burden, and create opportunities for errors.
The challenge facing health systems today is no longer centered around finding clinically accurate AI. It is orchestrating a reliable and scalable workflow around it. With systems like the Dell Pro Precision with GB10 help to close the workflow gap, with the solution delivered on-premise. The governance first approach is matched by an infrastructure designed to keep data local and performance predictable.
The Integration Gap
For many clinicians, the promise of AI is overshadowed by what 3DR Labs calls the “integration gap”, the effort required to ensure the right study reaches the right algorithm at the right time. Every manual handoff in the workflow is a potential failure point, which can equate to more time needed to treat the patient. Agentic AI eliminates that gap by interpreting orders, encounter notes, and EHR data and automatically routing studies to the appropriate algorithm, all while the data never leaves the organization’s firewall.
Consider a critical stroke case. The optimal AI for diagnosing a potential stroke, where speed to treatment is vital, is likely different from the advanced visualization needed to assess a trauma case. Manually interpreting intent and then routing the correct imaging series to the right systems is prone to error and delay. In healthcare, every minute matters.
This is the problem Agentic AI was built to solve.
From Task-Specific Tools to Governed Orchestration
Agentic AI represents a fundamental shift from tools that do one thing well, to systems that understand clinical context and can act within defined permissions. Rather than waiting for user input, agentic AI proactively interprets clinical intent from imaging orders, encounter notes, EHR data, and then initiates the approved actions within the workflow.
3DR Labs has operationalized this shift through Strings, a governed Agentic AI platform purpose-built for enterprise imaging. Strings anticipates clinical intent, learns from enterprise-wide workflow patterns, and orchestrates governed action across systems, automating manual processes and resolving bottlenecks before they impact patient care.
The platform functions as a contextually aware digital teammate that continuously supports clinical, operational, and technical decision-making. Its intent-based intelligence engine evaluates workflows for protocol discrepancies and ambiguous orders, helping ensure repetitive tasks like clarifying orders, extracting measurements, and syncing patient metadata can be handled within approved workflows.
This reduces the cognitive load that drives burnout and fragments a clinician’s day. In the case of a critical stroke or trauma emergency, Strings automatically routes the appropriate image series to the correct algorithm based on the clinical context of the patient encounter, potentially cutting minutes from the time to diagnosis. Clinicians remain responsible for clinical decisions; ambiguous orders should be escalated for review, with agent actions logged for oversight.
Real-World Impact
Health systems are already seeing measurable results with Strings. At one large, multi-site health system generating over 1 million imaging studies annually, 3DR Labs reports that Strings eliminated more than 467 hours per month of physician and nurse time by autonomously handling the manual steps between EHR and mobile clinical apps that previously required clinicians to search, verify, and sync patient data by hand. Enterprise imaging leadership at the organization described the shift as one that "permanently reduces support labor costs" while freeing teams to focus on continuous quality improvement and strategic resource planning.
At a major academic medical center operating across diverse modalities and vendor systems, Strings established unified audit visibility that brought long-standing operational blind spots to light by allowing leadership to identify and remediate site-by-site workflow bottlenecks in real time.
When modeled against a large health system generating one million studies annually, the financial impact is substantial with clinical, IT, and workflow automation agents collectively projected to deliver over $1.7M in annual savings by reclaiming thousands of hours of staff time and shifting IT from reactive to strategic.
Governed AI on an Enterprise Foundation
What sets Strings apart is its approach to responsible AI at scale. When an FDA-cleared algorithm processes an imaging study, Strings automatically captures and records usage, turnaround time, and results in support of AI governance. The platform integrates with EHR, PACS, and radiology reporting systems through secure DICOM, HL7, and FHIR communications, with an Imaging Labs Gateway that deploys in less than 60 seconds.
This governance-first approach is matched by an infrastructure designed to keep data local and performance predictable. Strings is validated to run on the Dell Pro Precision with GB10, part of the Dell Deskside Agentic AI portfolio within the Dell AI Factory with NVIDIA. The GB10 is a compact, enterprise-grade system that enables Strings to bring agentic AI directly to the point of work, delivering dedicated local compute, greater data control, and predictable per-study compute costs without dependency on cloud-only infrastructure. Strings is leveraging NVIDIA NIM, part of NVIDIA AI Enterprise, to run optimized AI inference services locally. NVIDIA NIM provides containerized, GPU-accelerated model serving, giving Strings an enterprise-ready foundation for deploying the AI models that support its clinical, operations, and technical agents.
Local AI brings selected agentic workflows closer to sensitive imaging data, specialized applications, and clinician review. Right-sized local compute can reduce remote-inference latency and make repeated inference costs more predictable. IT teams govern model and tool access, audit trails, and data residency, while choosing models suited to the task. Teams can evaluate workflows locally and scale higher-volume workloads to shared enterprise or cloud infrastructure according to performance, capacity, and governance requirements. For health systems, this architecture enables sensitive imaging data and agentic workflows to remain within the organization’s environment while reducing dependency on cloud-only AI inference.
Bringing the Focus Back to the Patient
The future of medical imaging is not defined by the number of AI algorithms a healthcare organization owns, but how effectively those tools are woven into the fabric of care. Agentic AI represents a way to reduce the integration tax, the manual searching, tagging, and routing that has long fragmented the clinician’s day.
By transforming disjointed workflows into a single, intent-aware system, agentic AI ensures that critical findings reach the physician, that point-of-care images are never lost, and that the most urgent cases are automatically elevated within clinician-approved protocols.
Ultimately, it restores the clinician’s primary role, moving past the administrative friction of the screen to focus entirely on the person under care.
Discover how the Dell Pro Precision with GB10 can accelerate your imaging AI strategy while keeping data under your control.
