Table of Contents

The Evolution of Medical Imaging Infrastructure

Picture Archiving and Communication Systems (PACS) have served as the backbone of digital medical imaging for decades, replacing physical film libraries with digital storage, retrieval, and distribution of radiologic images. These systems transformed radiology workflows by enabling radiologists to view images on workstations rather than light boxes and eliminating the logistical burden of managing physical films. However, traditional PACS architectures were designed in an era when on-premises hardware was the only viable option, and these legacy systems now face mounting pressure to keep pace with exponential data growth, distributed healthcare networks, and the demand for real-time collaboration across institutions.

The next evolutionary leap for PACS lies in embedded cloud computing capabilities, where cloud infrastructure is not merely an adjunct or external archive but is integrated directly into the core architecture of the system. This integration fundamentally shifts how imaging data is stored, accessed, processed, and shared. Rather than treating the cloud as a distant backup repository, embedded cloud PACS weave cloud-native services into the fabric of daily operations, enabling seamless scalability, advanced analytics, and ubiquitous access without the operational overhead of maintaining massive on-site data centers.

As healthcare organizations grapple with rising imaging volumes, workforce shortages, and the imperative to deliver faster diagnoses, the transition toward cloud-embedded PACS represents a strategic necessity rather than a technological luxury. Radiologists, referring physicians, and administrators are increasingly expecting the same level of flexibility and performance from medical imaging systems that consumer cloud services provide in their personal lives. Understanding the architecture, benefits, challenges, and future trajectory of these systems is essential for healthcare leaders making procurement and modernization decisions.

Defining Embedded Cloud Computing in PACS

Embedded cloud computing refers to the deep integration of cloud-native services directly into the PACS software stack, as opposed to simply storing images in cloud object storage or using a cloud gateway for off-site backup. In an embedded cloud PACS, the system is built from the ground up to leverage cloud infrastructure for core functions including image ingestion, processing, storage, retrieval, sharing, and disaster recovery. The cloud is not an external system bolted onto an on-premises PACS—it is the operational environment.

This approach differs significantly from hybrid models where some components remain on-premises while others reside in the cloud, often requiring complex synchronization and data movement between environments. Embedded cloud PACS unify these layers, allowing for a single, cohesive workflow where images are immediately available across all authorized locations without the latency of copying data between silos. The embedded model also facilitates the integration of compute-intensive capabilities such as artificial intelligence inference, advanced visualization, and multi-site collaborative reading, which become native features rather than add-on services.

Key Architectural Components

An embedded cloud PACS typically includes several interrelated components designed to work together as a unified platform. The cloud-native data layer uses scalable object storage, often with geo-redundant replication, ensuring that imaging studies are durable and accessible from any location with network connectivity. Compute resources are dynamically allocated for image processing tasks such as reformatting, 3D reconstruction, and AI inference, scaling up during peak hours and scaling down during idle periods to optimize cost.

Identity and access management is handled through enterprise-grade authentication and authorization services, integrating with existing single sign-on (SSO) systems and supporting role-based access controls that comply with healthcare privacy regulations. Application servers run containerized or serverless workloads, allowing the system to be updated and patched without downtime. The user interface is delivered via web browsers or thin clients, eliminating the need for dedicated, expensive workstations and enabling radiologists to read studies from any device with sufficient display quality and bandwidth.

Distinction from Cloud-Connected and Hybrid Systems

It is important to differentiate embedded cloud PACS from earlier approaches. Cloud-connected PACS typically keep the primary archive and reading workflow on-premises while using the cloud for long-term storage or disaster recovery. Hybrid systems split workloads, with some sites using the cloud for backup and others relying on local servers. In contrast, embedded cloud PACS do not have a primary on-premises archive—the cloud is the primary operational environment. Local caching may still exist for performance optimization, especially in bandwidth-constrained environments, but the authoritative copy of every study resides in the cloud.

This architectural distinction has profound implications for how organizations manage their imaging operations. The embedded model reduces the administrative burden of maintaining redundant hardware, simplifies software updates (which are managed centrally by the vendor), and ensures that all users are working with the same version of the system and the same set of images. It also enables a level of business continuity that is difficult to achieve with on-premises systems, as the cloud provider's infrastructure is designed for high availability and disaster resilience.

Primary Benefits for Healthcare Organizations

Unrestricted Accessibility and Distributed Reading

The most immediately visible benefit of embedded cloud PACS is the ability for radiologists and referring physicians to access imaging studies from virtually anywhere. This capability is not merely about convenience—it directly supports the growing demand for teleradiology, remote specialist consultations, and multi-site healthcare networks. A radiologist covering multiple hospitals can access the same PACS interface regardless of whether they are at home, at a tertiary care center, or at a rural clinic. The system maintains a consistent experience, with all studies, reports, and clinical context available in real time.

For referring physicians, cloud-based access means that imaging results are available at the point of care without requiring the patient to carry a CD or USB drive. Emergency department clinicians can view a recent CT scan from a tablet while consulting with a radiologist on a separate device. This seamless accessibility reduces time to diagnosis, minimizes redundant imaging, and improves care coordination, especially in complex cases involving multiple specialists across different locations.

Cost Efficiency and Capital Expenditure Reduction

Traditional on-premises PACS require significant upfront capital investment in servers, storage arrays, networking equipment, and uninterruptible power supplies. These systems also demand ongoing operational expenditures for maintenance contracts, hardware replacements, cooling, physical security, and the personnel needed to manage the infrastructure. Embedded cloud PACS shift cost from capital to operational models, with predictable subscription or usage-based pricing that eliminates the need for large upfront hardware purchases.

Healthcare organizations can redirect resources previously allocated to IT infrastructure toward clinical innovation, staffing, or patient care initiatives. Smaller hospitals and imaging centers that could not justify the expense of a fully redundant on-premises PACS can now access enterprise-class imaging capabilities through cloud subscriptions. The economic scale of cloud providers also means that storage and compute costs continue to decrease over time, whereas on-premises hardware requires periodic forklift upgrades that are both costly and disruptive.

Elastic Scalability for Growing Imaging Volumes

Medical imaging volumes are growing at a compound annual rate driven by population aging, expanded screening guidelines, and the increasing use of advanced modalities such as CT, MRI, and PET. Traditional PACS must be provisioned for peak capacity, leading to periods of underutilization and periodic upgrade cycles. Cloud-embedded systems, by contrast, scale elastically. Storage automatically expands as new studies are ingested, and compute resources are allocated dynamically for processing and serving images.

This elasticity is particularly valuable for organizations that experience volume spikes due to seasonal diseases, public health emergencies, or large-scale screening programs. Instead of planning for maximum capacity and paying for idle infrastructure during normal periods, organizations pay only for what they use, with the cloud provider managing the underlying resource allocation. This capability also simplifies capacity planning for mergers and acquisitions, as newly acquired facilities can be onboarded onto the cloud PACS without procuring additional hardware.

Enterprise-Grade Data Security and Compliance

Contrary to early concerns that cloud storage is inherently less secure than on-premises systems, leading cloud providers invest heavily in security infrastructure that most healthcare organizations cannot match individually. Encryption at rest and in transit, granular access controls, continuous threat monitoring, and automated compliance auditing are standard features in cloud environments. Cloud platforms also maintain certifications such as HIPAA, SOC 2, ISO 27001, and HITRUST, providing a compliance framework that can be leveraged by the healthcare organization.

Embedded cloud PACS can implement advanced security features that are difficult to achieve in on-premises systems, such as geofencing, anomaly detection, and immutable audit logs. Data sovereignty requirements can be addressed by selecting cloud regions that correspond to regulatory jurisdictions. In the event of a ransomware attack, cloud-based systems with immutable backup policies can restore data quickly without paying ransoms, whereas on-premises archives may be completely compromised if attackers gain access to the local network.

Continuous Innovation and Feature Velocity

On-premises PACS upgrades are typically infrequent, expensive, and disruptive, often requiring downtime for installation and validation. Embedded cloud PACS allow vendors to release new features, security patches, and performance improvements continuously without requiring manual intervention from the customer. This means that all users benefit from the latest capabilities as soon as they are available, rather than waiting for an annual upgrade cycle that may never be fully implemented due to resource constraints.

The cloud-native architecture also enables integration with a growing ecosystem of third-party services, including AI algorithms for image analysis, structured reporting tools, and enterprise imaging platforms. These integrations can be deployed and updated centrally, reducing the burden on local IT teams and ensuring that best-of-breed technologies are available across the entire organization.

Addressing the Challenges and Risks

Regulatory Compliance and Data Sovereignty

Healthcare is heavily regulated, and the storage and transmission of protected health information (PHI) in the cloud requires meticulous attention to compliance. Laws such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States, the General Data Protection Regulation (GDPR) in Europe, and local data residency requirements impose obligations on covered entities and business associates. Organizations must ensure that their cloud PACS vendor signs a business associate agreement (BAA) and that data is stored in approved geographic regions.

The challenge is compounded for multinational healthcare organizations or those serving patients from multiple jurisdictions. They must navigate varying requirements regarding data localization, patient consent, breach notification timelines, and enforcement mechanisms. Embedded cloud PACS vendors must provide transparent documentation of their compliance posture, undergo third-party audits, and offer configuration options that allow organizations to meet their specific regulatory obligations. The burden of compliance ultimately remains with the healthcare provider, making vendor due diligence a critical step in the selection process.

Technical Integration with Existing Systems

Few healthcare organizations operate in a greenfield environment. Most have existing investments in radiology information systems (RIS), electronic health records (EHR), speech recognition, and reporting tools. Migrating to an embedded cloud PACS requires integration with these systems, often through Health Level Seven (HL7) and Digital Imaging and Communications in Medicine (DICOM) interfaces. These integrations must be carefully designed to maintain data integrity, workflow continuity, and user experience during and after the migration.

Integration complexity varies depending on the maturity of the existing IT ecosystem, the availability of APIs in legacy systems, and the extent to which workflows rely on custom configurations. Organizations may need to update their RIS or EHR interfaces, retrain staff, and temporarily operate hybrid workflows during the transition period. Vendors that offer pre-built connectors and professional services for migration can significantly reduce this friction, but the organization's internal IT team must still be actively involved to ensure that configuration aligns with local clinical practices.

Network Latency and Performance Considerations

Radiologists are accustomed to sub-second image loading times on local workstations connected to high-speed local area networks. In a cloud-embedded model, image data must traverse wide area networks, introducing latency that varies based on bandwidth, distance to the cloud region, and network congestion. For modalities that generate large series of thin-slice images, such as CT and MRI, the total data volume per study can be substantial, and efficient transmission is critical for maintaining productivity.

Several strategies mitigate this challenge. Image streaming techniques load only the slices or sequences that the user views first, with progressive loading of additional data in the background. Compression algorithms, both lossless and visually lossless, reduce the amount of data that must be transmitted. Local caching at the reading location stores recently accessed studies, reducing the need for repeated downloads. Vendors may also deploy edge nodes within the healthcare network to cache studies and process image transformations locally, providing the performance of on-premises systems with the management benefits of cloud architecture.

Organizations considering embedded cloud PACS must conduct thorough network assessments to ensure that their wide area network connectivity meets the requirements for clinical use. Dedicated connections such as MPLS or AWS Direct Connect can provide consistent performance compared to internet-based connections, but they add cost. The decision to invest in network upgrades must be weighed against the operational savings from eliminating on-premises hardware.

Vendor Selection and Lock-In Risk

Selecting a cloud PACS vendor involves evaluating not only the functional capabilities of the imaging application but also the underlying cloud platform, the vendor's financial stability, and their commitment to interoperability. Organizations must consider the risk of vendor lock-in, where migrating to a different platform becomes prohibitively difficult due to proprietary data formats, APIs, or workflows. Standards-based approaches that use DICOM and FHIR for data exchange and avoid proprietary storage formats can mitigate this risk.

It is also important to understand the vendor's disaster recovery architecture, uptime guarantees, and processes for data export in the event of contract termination. Healthcare organizations should negotiate clear terms regarding data ownership, access rights, and migration assistance. Some organizations adopt a multi-cloud strategy or maintain an in-house archive of finalized studies as a safeguard, though this adds complexity and may partially offset the cost savings of the cloud model.

Impact on Radiology Workflows and Clinical Practice

Redefining the Reading Environment

Embedded cloud PACS fundamentally change where and how radiologists work. The traditional model of a radiologist reporting from a specialized workstation within a hospital reading room is giving way to a model where the radiologist can be in any location with adequate network connectivity and a suitable display. This shift has major implications for staffing, productivity, and work-life balance. Radiologists can cover multiple sites from a single reading session, reducing the need for subspecialists to travel or for hospitals to contract separate coverage for each location.

The reading environment becomes more flexible, but it also places greater responsibility on the radiologist to ensure that their local network, display, and ambient lighting meet clinical standards. Cloud PACS vendors typically provide tools for display calibration verification and recommended bandwidth specifications. Healthcare organizations must develop policies for home reading, including requirements for internet connectivity, privacy, and ergonomics, to maintain quality and compliance.

Real-Time Collaboration and Subspecialty Consultations

Complex cases often benefit from multiple readers, and cloud-native PACS enable real-time collaboration more naturally than on-premises systems. Radiologists can share a viewport with a colleague, scroll through images synchronously, and annotate findings while both see the same content. This capability supports education, quality assurance, and second opinions without requiring both participants to be in the same room or even on the same network.

Subspecialty consultations become faster and more accessible. A community hospital that does not have a dedicated neuroradiologist can request a real-time or near-real-time consultation from a specialist at an academic medical center. The cloud PACS provides the infrastructure for this exchange without requiring the two institutions to set up separate data sharing agreements or integrate their on-premises archives. The embedded cloud architecture makes this collaboration as natural as if both radiologists were using the same local system.

Artificial Intelligence as a Native Capability

Artificial intelligence is one of the most transformative technologies in medical imaging, and embedded cloud PACS are uniquely positioned to integrate AI into routine workflows. AI inference algorithms can process images as soon as they are ingested, providing radiologists with prioritized worklists, automated measurements, and triage notifications. For example, a cloud PACS can automatically run a stroke detection algorithm on every CT head study, marking positive cases for immediate review and sending alerts to the reading radiologist and the stroke team.

The advantages of running AI in the cloud include access to GPU compute resources that scale with demand, simplified deployment of algorithm updates, and the ability to test multiple algorithms against the same dataset without managing local GPU infrastructure. Cloud-based AI also facilitates the creation of feedback loops, where radiologist input improves algorithm performance over time. As regulatory approval for AI algorithms continues to expand, embedded cloud PACS will become the primary platform for delivering these insights at the point of care.

Technical Considerations for Implementation

Network Architecture and Bandwidth Planning

A successful cloud PACS deployment depends on a network architecture that provides sufficient bandwidth, low latency, and reliability for imaging workloads. Organizations should conduct traffic analysis to understand current imaging data flows and project future growth. Studies from high-volume modalities such as CT, MRI, and digital breast tomosynthesis generate large files that must be uploaded to the cloud for ingestion and processed studies must be streamed to reading locations.

Best practices include redundant internet connections from different carriers, dedicated bandwidth for imaging traffic, and Quality of Service (QoS) policies that prioritize real-time image streaming over less time-sensitive traffic. For organizations with multiple facilities, a private wide area network or software-defined WAN (SD-WAN) can provide consistent performance and centralized management. Cloud providers also offer direct connect services that bypass the public internet, providing lower latency and higher reliability for a fixed monthly cost.

Identity Management and Access Control

Embedded cloud PACS must integrate with the healthcare organization's existing identity management infrastructure, typically through SAML, OAuth, or LDAP protocols. Single sign-on (SSO) allows clinicians to access the PACS using their existing credentials, reducing password fatigue and improving security. Role-based access control ensures that users see only the studies and functionality appropriate to their job function, and audit logging provides a tamper-proof record of every access event.

Multi-factor authentication (MFA) should be enforced for all PACS access, especially for remote reading and administrative functions. Cloud PACS vendors should support integration with the organization's MFA provider, whether that is a cloud-based service or an on-premises solution. Emergency access procedures must be defined to allow critical access in the event of identity provider failure, with appropriate audit trails to monitor such exceptions.

Data Migration and Go-Live Planning

Migrating an active PACS archive containing millions of studies from an on-premises system to the cloud is a complex project that requires careful planning. The migration strategy must account for the total data volume, network bandwidth, and the need to maintain clinical availability during the transition. Common approaches include phased migrations by modality, date range, or facility, with each phase validated before proceeding.

Pre-staging data using physical media delivery (shipping hard drives to the cloud provider) can accelerate the initial transfer for large archives. Once the data is in the cloud, incremental synchronization keeps the archive up to date until the cutover date. A comprehensive testing plan should validate that images load correctly, that DICOM metadata is preserved, that integration with RIS and EHR systems functions properly, and that disaster recovery procedures work as designed. End-user training and change management are equally important, as radiologists and technologists must adapt to the new interface and workflows.

Interoperability and Data Exchange

Standards-Based Integration

Interoperability remains one of the most challenging aspects of healthcare IT, and imaging is no exception. Embedded cloud PACS must support industry standards including DICOM for image storage and transfer, HL7 v2 for orders and results, and FHIR for emerging use cases such as patient access to images and imaging-related data elements. Compliance with the Integrating the Healthcare Enterprise (IHE) profiles, such as Scheduled Workflow and Cross-Enterprise Document Sharing for Imaging (XDS-I), ensures that the system can interoperate with other compliant systems across different vendors and facilities.

Cloud-native architectures can actually improve interoperability compared to on-premises silos. The cloud environment makes it easier to implement APIs that enable secure, standards-based data exchange with external systems. For example, a cloud PACS can expose a FHIR API that allows an EHR to retrieve imaging metadata and view image thumbnails directly within the patient record, improving clinician access to imaging results without requiring a separate login.

Vendor Neutral Archive Considerations

Some organizations choose to implement a vendor neutral archive (VNA) strategy to decouple image storage from the PACS application itself. In a cloud context, a VNA can be deployed as a cloud-native service that stores images in standard formats and exposes standard APIs. The embedded cloud PACS then acts as a reading application that accesses the VNA. This architecture provides flexibility to switch reading applications without migrating image data, reducing vendor lock-in risk.

Cloud-based VNAs can also serve as a consolidation point for images from multiple PACS across a health system, providing a unified repository that supports enterprise-wide image access. The cloud VNA can integrate with other systems such as EHRs, patient portals, and AI platforms, creating a comprehensive enterprise imaging ecosystem. For health systems that have grown through acquisitions and have multiple legacy PACS, a cloud VNA strategy combined with an embedded cloud PACS reading application can unify the imaging environment while retiring outdated on-premises systems.

The Evolving Vendor Landscape

The market for PACS has traditionally been dominated by a handful of established vendors, but the shift to cloud-native architectures is changing the competitive dynamics. Incumbent vendors are investing in cloud versions of their products, while new entrants that were born in the cloud are gaining traction with modern architectures and subscription-based pricing. Some cloud providers, such as Amazon Web Services and Microsoft Azure, offer specialized healthcare imaging solutions that serve as platforms for independent software vendors to build upon.

Healthcare organizations evaluating vendors should consider the maturity of the cloud platform, the vendor's track record in healthcare, support for regulatory compliance, and the depth of their integration ecosystem. References from other healthcare organizations that have made similar transitions are invaluable. The selection process should also evaluate the total cost of ownership over a multi-year horizon, including subscription fees, network costs, integration services, and any retained on-premises components.

Open Source and Community Alternatives

Open source PACS solutions have traditionally been limited to research and small-scale deployments, but cloud-native open source projects are emerging that offer enterprise features. These platforms can be deployed on cloud infrastructure, providing a lower-cost alternative to commercial solutions for organizations with strong technical capabilities. However, they require significant in-house expertise for implementation, customization, and support, and the responsibility for compliance and security rests entirely with the deploying organization.

For most healthcare organizations, a commercial cloud PACS vendor provides the necessary guarantees regarding uptime, security, compliance, and support. Open source solutions may be appropriate for academic medical centers with dedicated imaging informatics teams that can manage the operational burden, but the total cost of ownership when factoring in personnel time may not be lower than a commercial subscription for a feature-complete platform.

Implementation Roadmap for Healthcare Leaders

Assessment and Strategy Development

Healthcare organizations considering a transition to embedded cloud PACS should begin with a thorough assessment of their current environment, including hardware lifecycles, storage volumes, network infrastructure, integration dependencies, and clinical workflows. This assessment informs a business case that compares the total cost of ownership of the current on-premises system with a cloud-native solution over a five- to ten-year timeline. The business case should account for not only direct costs but also opportunity costs related to IT staff time, downtime, and the ability to adopt new capabilities.

Stakeholder engagement is critical. Radiologists, technologists, IT leaders, and compliance officers must all have input into the requirements and be informed about the benefits and trade-offs of the cloud model. Change management planning should begin early, with clear communication about timeline, training, and support. Pilot deployments with a single modality or location can validate the technology and workflows before enterprise-wide rollout.

Phased Migration and Optimization

Most organizations benefit from a phased migration approach that starts with less complex workflows and lower volume modalities before moving to high-volume areas such as CT and MRI. Each phase should include defined success criteria, a rollback plan, and post-go-live support. After migration, organizations should continue to optimize their cloud environment by monitoring performance, adjusting resource allocation, and taking advantage of new features released by the vendor.

Ongoing governance ensures that the cloud PACS continues to meet clinical needs, compliance requirements, and financial objectives. Regular reviews of usage patterns, storage growth, and subscription costs help organizations right-size their cloud footprint and identify opportunities for further optimization. As the cloud PACS matures, organizations can expand their use of additional cloud services such as AI inference, analytics dashboards, and patient-facing portals.

Future Trajectories and Emerging Capabilities

Deep Integration with EHR and Longitudinal Patient Records

The future of embedded cloud PACS extends beyond radiology to encompass the broader concept of enterprise imaging, where all medical images from all modalities and departments are managed in a single, cloud-native platform. This includes images from cardiology, ophthalmology, pathology, dermatology, and other specialties. A unified enterprise imaging platform in the cloud enables a comprehensive view of the patient's imaging history, supports cross-specialty collaboration, and provides consistent tools for viewing and analysis.

Integration with the EHR deepens as images and derived data become more accessible within clinical workflows. Rather than launching a separate PACS viewer, clinicians will access images through the EHR interface, with the cloud PACS providing the rendering and interaction capabilities behind the scenes. This seamless integration reduces context switching, speeds decision-making, and ensures that imaging findings are considered alongside laboratory results, clinical notes, and medication histories.

Advanced Analytics and Population Health Management

Cloud-native PACS generate rich metadata about imaging utilization, turnaround times, reporting patterns, and clinical outcomes. Aggregating this data across the enterprise enables analytics dashboards that help radiology leaders optimize operations, identify bottlenecks, and benchmark performance against peers. Machine learning models can analyze this data to predict no-show rates, recommend protocol optimization, and identify patterns that signal quality improvement opportunities.

At a population health level, de-identified imaging data from cloud PACS can support research, clinical trials, and public health surveillance. The scalability of cloud infrastructure makes it feasible to analyze large imaging datasets that would be impractical to process on-premises. These capabilities are still emerging, but they point toward a future where imaging data contributes not only to individual patient care but also to broader insights that improve healthcare delivery across populations.

Sustainability and Environmental Impact

On-premises data centers consume substantial amounts of electricity for both computing and cooling, contributing to the healthcare industry's carbon footprint. Cloud providers are investing heavily in renewable energy and energy-efficient infrastructure, often achieving lower carbon emissions per unit of compute than typical enterprise data centers. For healthcare organizations with sustainability goals, migrating imaging workloads to the cloud can be part of a broader strategy to reduce environmental impact while simultaneously improving operational capabilities.

While the environmental benefit varies based on the cloud provider's energy mix and the efficiency of the organization's existing data center, the trend is clear: large-scale cloud infrastructure is more energy-efficient than most on-premises alternatives. As carbon reporting requirements become more common, healthcare organizations will increasingly factor the environmental impact of their IT infrastructure into procurement decisions.

Conclusion

Embedded cloud computing capabilities represent the most significant transformation in PACS architecture since the transition from film to digital. By integrating cloud-native services directly into the core imaging platform, healthcare organizations can achieve levels of accessibility, scalability, and innovation that are difficult or impossible to attain with traditional on-premises systems. The benefits extend beyond cost savings to include improved clinical collaboration, faster access to advanced technologies such as AI, and enhanced disaster resilience.

The transition is not without challenges. Regulatory compliance, network performance, integration complexity, and vendor selection all require careful attention. Organizations that invest in thorough planning, stakeholder engagement, and phased implementation will be better positioned to realize the full potential of cloud-native medical imaging. As the technology continues to mature and as more imaging workloads move to the cloud, embedded cloud PACS will become the standard for delivering high-quality, accessible, and secure medical imaging services.

Healthcare leaders should begin assessing their current infrastructure, developing migration strategies, and evaluating vendor capabilities now to ensure they are prepared for the cloud-native future of medical imaging. Those who delay risk falling behind in an environment where the ability to provide fast, accurate, and collaborative diagnostic imaging is increasingly tied to the underlying technology platform. The future of PACS is in the cloud, and that future is already taking shape in the most forward-looking imaging organizations around the world.