White Paper: Building a Practical Architecture for Healthcare AI

Executive summary

Healthcare organizations are transitioning from AI prototypes to production-grade AI systems. Instead of simply identifying promising use cases, they are building the technical foundation required to run AI reliably across imaging, clinical and operational environments.

That foundation has to account for healthcare’s unique characteristics, which include distributed data, unstructured content, performance-sensitive workflows, deployment constraints and the need for stronger governance as AI becomes more deeply embedded in day-to-day operations. Today, many provider organizations have already validated that AI can deliver value and are now planning how to architect for scale without adding unnecessary complexity.

This white paper examines the technical requirements for production healthcare AI, including data readiness, workload placement, inference design, storage, networking and operational management.

From AI interest to AI infrastructure

Early AI conversations often center on possibilities, but production conversations are granular and pragmatic. They focus on what is required to sustain performance, maintain control, integrate with existing systems, and expand beyond a narrow pilot.

In healthcare, production conversations are especially complicated because systems are decentralized and complex. Data is fragmented across many systems, and content may be unstructured or multimodal. Certain workflows require low latency, and many use cases depend on local control over sensitive information.

Once AI begins to support real decisions or high-volume processes, architecture becomes inseparable from outcomes. That is why healthcare AI should be treated as an infrastructure and systems design challenge.

Healthcare AI is now a systems design challenge

In the pilot stage, it is natural to focus on the model. Can it classify? Can it summarize? Can it improve throughput? Can it support clinicians with faster access to relevant information?

Those are valid questions, but they are not enough for production.

Production AI depends on whether the full environment can support the model in context. That includes:

• Access to the right data at the right time
• Sufficient compute for sustained inference
• Storage that can deliver large or complex data efficiently
• Networking that does not become a bottleneck
• Governance that supports control and accountability
• Operations that can scale across more than one use case

In healthcare, these requirements are magnified by the nature of the work. Imaging pipelines are data intensive and performance sensitive. Clinical support workflows depend on governed access to protected information. Operational use cases often involve multiple systems, real-time responsiveness, and enterprise coordination. The result is that healthcare AI becomes less about isolated model quality and more about system design.

This is why provider organizations need to architect AI as a full-stack capability, not treat it as an overlay added on top of existing systems.

The challenge is not just volume

It is tempting to describe healthcare as a big-data problem, but it’s more than that. Healthcare data is:

• Distributed across systems and domains
• Inconsistent in structure
• Difficult to govern uniformly
• Shaped by workflow-specific access patterns
• Often unstructured or multimodal

These characteristics make AI more difficult to operationalize than simple data volume would suggest.

The data layer determines what AI can do

Healthcare organizations collect data from many, disparate systems. But that data is fragmented and not consistently available to AI.

For example, imaging data sits in one set of repositories. Clinical records and reports sit in another. Operational and patient access data are maintained elsewhere. Some data is generated and consumed locally at the edge. As a result, the context required for AI is often present, but not easy to access in a coordinated, repeatable way.

Unstructured and multimodal data raise the bar

Many of the highest-value AI use cases in healthcare depend on data that is not neatly organized into tables, such as images, dictated notes, reports, scans, PDFs, voice interactions, and other complex content types. They all require some combination of transformation, enrichment, indexing and governance before they can support inference pipelines.

This means the data layer must go beyond storing information and make that information usable.

AI-ready data is an active capability

To support production AI, the data environment should enable:

• Discovery of relevant sources
• Preparation and transformation of raw content
• Metadata and contextual enrichment
• Governed retrieval Support for multimodal access patterns
• Efficient delivery into inference workflows

If these capabilities are weak, AI remains constrained. A model may be technically strong, but its outputs will still be limited by what the surrounding system can provide.

Workload placement is a core architecture decision

Where AI runs is one of the most important design decisions in healthcare.

That decision affects:

• Latency
• Privacy posture
• Data movement costs
• Operational fit
• Scalability
• User experience

There is no universal answer. Not every healthcare AI workload belongs in the same environment.

Local and edge-oriented deployment

Some workloads are best placed close to the data or close to the user. This is often true when:

• Large files are expensive or inefficient to move
• Low latency is important
• Sensitive data should remain under tighter institutional control
• Local teams need direct access for development or inference
• Workflows are departmental or site-specific

Imaging, research and certain clinical support scenarios often fit this model well.

Core or private cloud deployment

Other workloads are better served in centralized infrastructure. This is often the right fit when organizations need:

• Shared model-serving capabilities
• Governed enterprise access
• Integration across multiple systems
• Centralized operational management
• Higher-throughput inference services

Hybrid deployment

In practice, many provider organizations need a hybrid placement model.

Some data and workflows should remain local. Others benefit from centralized coordination. The technical objective is to create a consistent architecture that supports placement flexibility without fragmenting the operating model.

Inference becomes the operational center of gravity

In healthcare AI, long-term success depends on sustained inference. It happens every day, often at scale, and it powers image analysis, search, summarization, workflow classification, knowledge retrieval and many other forms of AI-enabled work.

Why inference matters more in production

Once AI becomes part of real workflows, inference creates the recurring load that shapes architecture decisions. It influences:

• Compute utilization
• Storage access patterns
  Networking demand
• Observability requirements
• Operational cost
• System responsiveness

That is why production environments should be designed around inference first.

Inference patterns are becoming more complex

Modern healthcare AI workflows often involve more than a single request and response. They may include retrieval steps, contextual grounding, intermediate reasoning, tool invocation and follow-on actions. This increases the burden on the surrounding stack.

As these workflows become richer, the need for coordination across compute, storage and networking becomes more pronounced. This is another reason why AI infrastructure must be designed as an integrated environment rather than a set of disconnected layers.

Medical imaging demonstrates what production AI really requires

Medical imaging remains one of the clearest examples of AI’s potential in healthcare and one of the clearest examples of why architecture matters.

Imaging workflows are demanding. They involve large data objects, heavy throughput, timesensitive interpretation, and close integration with clinician workflows. AI can create significant value in this environment, but only when the surrounding infrastructure supports data access, inference performance and operational fit.

Northwestern Medicine achieved up to a 40% productivity improvement in radiology image review using Dell AI Factory with NVIDIA deployed on premises.1 It’s more proof that when infrastructure is aligned with data locality, user needs, and institutional control, production AI can deliver measurable workflow gains.

Imaging also highlights the importance of distributed compute. Some tasks benefit from centralized infrastructure. Others benefit from workstation-class or desk-side environments, especially where users need faster iteration, tighter control over data, or specialized local performance.

The AI stack has to work as one system

A healthcare AI environment scales most effectively when compute, storage, networking, software and operations are designed as parts of one system.

PowerScale and ObjectScale are particularly relevant here, because they support the scale, access patterns, and control needed for healthcare AI environments handling large and complex data estates.

Compute must match the workload

Healthcare AI requires a range of compute models.

Dell Pro Max systems support local experimentation, desk-side inference and specialized workflows such as medical imaging and genomics. They are especially useful when local responsiveness, sensitive data handling, or high-performance user-side workflows are required.

At the same time, enterprise-scale AI depends on data center infrastructure that can support sustained inference and model serving. PowerEdge servers with NVIDIA acceleration provide that foundation for scalable healthcare AI workloads across imaging, analytics and broader enterprise environments.

The important design principle is heterogeneity. One compute tier is not enough. Provider organizations need a compute strategy that matches the workload rather than forcing all workflows into a single environment.

Storage must function as part of the inference path

Storage directly affects whether data can be retrieved, governed and served efficiently enough to support real workflows.

PowerScale and ObjectScale are particularly relevant here, because they support the scale, access patterns, and control needed for healthcare AI environments handling large and complex data estates. In a well-designed architecture, storage contributes to:

• Data accessibility
• Multimodal retrieval
• Governance and control
• Inference efficiency
• Scale over time

This is especially important in healthcare, where AI often depends on large data sets and mixed content types.

Dell AI Data Platform storage engines are built for fast, reliable AI data access, supporting the full data lifecycle and keeping training, inference, and retrieval workloads efficient.

Networking must be treated as performance infrastructure

As AI workloads scale, networking becomes central to overall system behavior. Inference-heavy environments generate repeated traffic between storage, compute and integrated services. If the network becomes constrained, system responsiveness degrades regardless of model quality.

SN-Series switches are important in this context because they provide the high-performance connectivity needed to move data efficiently across AI environments and support demanding workloads. Networking should therefore be designed with AI expansion in mind, especially in architectures where data movement and distributed inference are core patterns.

Software: the control layer that turns infrastructure into an AI platform

Software and automation are what make the Dell AI Factory with NVIDIA operate as a unified system rather than a collection of separate components. The software layer helps automate deployment, centralize lifecycle management and keep AI workloads consistent across core, edge and distributed environments.

For healthcare and life sciences, that matters because it reduces operational complexity and helps teams move AI workloads from pilot to production faster, with less manual effort and more repeatability.

NVIDIA AI Enterprise brings together microservices, frameworks and libraries for AI development with advanced GPU orchestration and infrastructure management in a fully supported, production-ready, commercial software suite.

Deploy leading open-source tools and AI models with confidence, improve productivity and time to value, and run AI workloads at scale with optimized resource utilization.

Governance and observability have to be engineered in

Governance in healthcare AI is not only a policy issue.

It is an engineering issue. As AI becomes more embedded in workflows, organizations need visibility into how systems access data, outputs are produced, and activity can be audited or corrected over time. This becomes more critical as use cases expand and interactions become more iterative.

A strong production design should support:

• Data access control
• Visibility into inference flows
• Exception handling
• Reviewability
• Repeatable oversight

Observability enables scale because it allows organizations to expand use while maintaining confidence in how systems behave.

Operational management becomes part of technical scalability

Even well-designed AI infrastructure can become difficult to scale if deployment and management remain fragmented or overly manual.

As organizations support more models, data flows, and integrated use cases, operational consistency becomes increasingly important. Dell Automation Platform helps address this challenge by supporting orchestration and streamlined operations across private cloud and distributed environments.

In practical terms, repeatability is a scaling advantage. The more consistent the environment, the easier it becomes to support new AI initiatives without recreating infrastructure decisions each time.

Dell AI Factory with NVIDIA is the architecture anchor

A healthcare AI strategy becomes more sustainable when it starts from a reusable platform foundation.

Dell AI Factory with NVIDIA provides that foundation by bringing together infrastructure, software and services in a framework designed to support repeatable deployment across use cases. In healthcare, that matters because organizations need to support different data types, placement models and workflow requirements without introducing unnecessary fragmentation.

The value of this approach is architectural. It creates a path to:

• Reuse core infrastructure decisions
• Align compute, storage, networking and software
• Support multiple deployment models
• Maintain more consistent governance
• Scale from one use case to many without rebuilding the environment each time

The broader Dell portfolio extends that core foundation across workstation, compute, storage, connectivity and operational management.

A practical technical roadmap

Organizations do not need to solve every healthcare AI challenge at once, but they do need a deliberate sequence.

Organizations do not need to solve every healthcare AI challenge at once, but they do need a deliberate sequence.

Scalable, reliable and observable infrastructure for healthcare AI

Healthcare AI is entering a phase where infrastructure decisions change outcomes.

Provider organizations need a technical foundation that can support distributed data, sustained inference, multimodal content, flexible placement and stronger governance. That foundation must be designed as an integrated system, not assembled as a series of isolated experiments.

Dell AI Factory with NVIDIA provides the core architecture for that system. Supported by Dell Pro Max systems, PowerEdge servers, PowerScale, ObjectScale, SN-Series switches and Dell Automation Platform, it enables a more integrated and repeatable approach to healthcare AI.

The organizations that scale successfully will be the ones that treat AI as a systems design challenge from the start.