March/April 2026 Issue
Imaging Informatics: Going Native
By Scott Miller
Radiology Today
Vol. 27 No. 2 P. 8
Reimagining the Future of Health Care Through Digitally Native Imaging
Radiology is a critical component of modern medicine, touching nearly 80% of all patient care journeys,1 guiding treatment decisions, and, ultimately, shaping patient outcomes. Due to its foundational role, the significant pressures the field faces must be addressed. Surging imaging volumes driven by an aging population and rising chronic diseases, coupled with persistent workforce shortages, create a widening gap between demand and capacity. This imbalance threatens the quality and timeliness of care, pushing systems to their limits.
This moment demands more than incremental adjustments—it calls for a bold reimagining of imaging itself. Today’s digitally native generations expect seamless, consumer-grade experiences, intuitive interactions, and instant access to information. Fragmented workflows, disparate, hardware-centric systems, and siloed data no longer align with these expectations, nor do they support comprehensive care. The path forward requires a fundamental transformation that moves imaging from its current state to connected, intelligent, and truly digitally native platforms that reduce friction, enhance outcomes, and empower every stakeholder across the care continuum.
Transformation Imperative
Health care has historically lagged behind other industries in digital transformation, due the complexities of patient data, regulatory requirements, and operational scale.2 However, the current environment necessitates change. A pervasive lack of connectivity across health systems leads to fragmented care, delayed diagnoses, and restricted access, as patient health data—from paper records and film to digital files, lab results, and handwritten notes—is often dispersed across separate systems and various unstructured formats when patients move between providers. This fragmentation means critical patient information is often siloed and inaccessible when and where it’s needed most.
Even within integrated networks, EHRs aren’t always fully integrated or standardized, and PACS frequently exhibit vendor or platform-specific limitations. This lack of a unified view prevents clinicians from gaining a holistic view of a patient’s health status, hindering optimal care delivery.
Moreover, escalating patient volumes and data complexity strain hospital IT infrastructure. This burden, combined with limited IT and financial resources, impedes health systems’ ability to scale and adapt. The consequence is operational inefficiency and increased staff burnout, especially in radiology departments facing fluctuating exam volumes and diverse case mixes. Addressing these challenges requires a shift from reactive measures to proactive, transformative solutions that consolidate data, streamline workflows, and support clinicians and technologists with robust, adaptive tools.
Human-Centered, Digitally Powered
The vision for imaging’s future is profound simplicity: clarity through innovation. This vision isn’t just about building advanced machines, it’s about designing experiences grounded in empathy, engineered for simplicity, and powered by digital intelligence. This calls for a strategic focus on four key pillars that will define the next generation of imaging ecosystems:
1. Intuitive: Future systems must offer seamless, user-friendly experiences that simplify complex workflows and reduce the cognitive load on providers and staff, moving toward one-click workflows and streamlined interfaces that allow clinicians to focus on patient care, not technology.
2. Human-Centered: Technology should be designed with empathy to reduce physical strain and enhance comfort for clinicians and patients throughout the care journey. Every touchpoint should prioritize wellbeing and ease.
3. Intelligent: AI-powered tools elevate diagnostic precision, enabling the prioritization of cases and scaling insights across all modalities and care settings, processing vast datasets to highlight subtle anomalies and supporting clinical decisions with unmatched consistency.
4. Enterprisewide: Unified platforms connecting data, devices, and disease pathways across the health system ensure systemwide efficiency, interoperability, and informed decision making by ensuring relevant patient information is accessible when needed.
This vision translates into less burden on clinicians, increased patient access, and seamless integration where systems truly “speak to each other” while empowering users with intuitive tools that amplify their capabilities.
The Power of Cloud and AI
Flexible, cloud-based software-as-a-service approaches are foundational to a digitally native future. Cloud technology can enable clinicians to access useful, actionable data precisely when and where needed, facilitating secure sharing of images, documents, and other patient data for efficient collaboration and integrated care.
Supporting connected care necessitates a holistic approach to enterprise imaging: a centralized, unstructured repository for imaging and patient data, capable of supporting multiple workflows across facilities, departments, and care pathways. Benefits of such cloud-based solutions include:
• Enhanced Operational Efficiency: Cloud-based storage and vendor-neutral archives can reduce IT costs by eliminating costly on-premise infrastructure. AI-standardized data migration integrates legacy systems seamlessly, minimizing clinician downtime. Hybrid cloud deployments offer cost-effective modernization, leveraging existing hardware while transitioning, avoiding disruptive capital reinvestments.
• Improved Patient Care and Workflows: Centralized medical images and data accessible across geographies improve operational efficiency and collaborative diagnostics. This streamlines imaging access, reduces diagnostic delays, and unifies patient imaging across providers and EHRs for coordinated care. Cloud solutions also accelerate the adoption of new imaging technologies, ensuring access to the latest diagnostic advancements.
• Expanded Patient Access: Cloud solutions eliminate traditional access barriers like geography, to help enable secure image sharing from anywhere. This provides remote care access for patients in rural and underserved areas, offers flexible scalability for growing volumes, and supports holistic care by providing clinicians with a complete patient health picture for personalized treatments.
AI acts as the pivotal engine within this evolving ecosystem. In radiology, where precision and speed are paramount, AI streamlines workflows, accelerates care pathways, and enhances diagnostic accuracy. AI-driven capabilities complement remote access systems by analyzing scan parameters, identifying deviations from standardized protocols, and suggesting real-time adjustments—invaluable features for complex cases or less specialized staff. By actively protecting data, eliminating bias, and preserving clinical judgment, AI can enhance human capabilities without replacing them.
A Call to Action
Digitally native, cloud-powered, and AI-driven platforms cut through the complexities of modern imaging, helping to resolve workflow inefficiencies, staffing shortages, and data fragmentation. They free up radiologists for high-value interpretation, simplify tasks for technologists, and arm administrators with data-driven insights, ensuring patients feel more informed, supported, and seen throughout their care journey.
The future of imaging is undoubtedly digital, connected, and deeply human-centered. This transformation isn’t a solitary endeavor. It requires a collaborative effort from executives, providers, technologists, and patients. By embracing this transformation, we can collectively build a health care system that’s smarter, faster, more compassionate, and, ultimately, delivers better patient care.
— Scott Miller is the president and CEO of solutions for enterprise imaging at GE HealthCare. He assumed this role in January 2025 and has over 28 years of experience at GE HealthCare in other positions.
References
1. Smith-Bindman R, Miglioretti DL, Johnson E, et al. Use of diagnostic imaging studies and associated radiation exposure for patients enrolled in large integrated health care systems, 1996-2010. JAMA. 2012;307(22):2400-9.
2. Davenport AM. Healthcare is historically slow to adapt to change: why clinical trials can’t afford it with AI. Clinical Researcher. 2025;39(4). Available at: https://acrpnet.org/2025/08/19/healthcare-is-historically-slow-to-adapt-to-change-why-clinical-trials-cant-afford-it-with-ai. Accessed December 2025.
