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Blog
Andy Pollen

How Foundation Models Will Redefine “Enterprise” AI in Healthcare

Adoption, Foundation Models, System Integration
Table of Contents

The term “enterprise-grade” is everywhere in healthcare AI. Often, it refers to marketplaces that bundle multiple point solutions, each model designed for a specific pathology. The more algorithms included, the more “enterprise” the offering appears.

However that definition only goes so far.

Bundling more algorithms may expand clinical coverage, but it also compounds complexity: more contracts and vendors to manage, more integrations to maintain and more validations to repeat. This model-by-model approach creates drag that limits scalability, slows adoption and fragments impact.

Where Platforms Already Add Value

A true AI platform – like Aidoc’s aiOS™ – already addresses these limitations. 

Instead of merely aggregating tools, it orchestrates them: 

  • Streamlining deployment across imaging and EHR systems
  • Standardizing performance monitoring
  • Delivering results directly into native workflows
  • Simplifying governance across sites, specialties and service lines

Today’s platforms let health systems scale AI more intelligently, but they’re still often powered by narrow, single-task algorithms. While that’s helped introduce the power of AI to care teams, the coverage remains limited. Even the most advanced platforms cover a small fraction of the pathologies visible on a typical imaging scan — often less than 3%.1

To truly transform clinical decision-making, we need a new approach.

The Limits of the Multi-model Marketplace

Many AI offerings today are still collections of independently trained, validated and integrated models. Even when bundled under a shared interface or contract through a marketplace, they remain siloed behind the scenes — each with its own regulatory, operational and support requirements.

This creates friction that grows exponentially with scale:

  • Each algorithm requires separate integration, testing and rollout
  • Licensing, support and IT oversight multiply with every deployment
  • No unified usage tracking or performance validation
  • Clinical coverage expands slowly, one model at a time

What looks scalable on paper often stalls in practice and ultimately falls short of delivering the kind of support clinicians actually need.

Foundation Models: The Next Generation of Clinical Intelligence

Foundation models represent the next generation of enterprise AI. These large, generalizable models are pretrained on massive, multimodal datasets — including imaging, notes, labs and more — using self-supervised learning techniques. 

Instead of learning one task at a time, they build broad clinical understanding that can be fine-tuned for dozens of use cases across specialties.

This isn’t just “one model with many outputs.” It’s a fundamentally new pathway to scale.

A single foundation model trained on chest imaging, for example, can detect suspected pulmonary embolism (PE), pneumonia, lung nodules, cardiac strain and more — all from a single scan. As new data becomes available, it can be fine-tuned for new indications in days or weeks, not months or years.

The result is faster development, lower technical burden and a more scalable path to comprehensive clinical support across radiology, cardiology, neurology and beyond.

From Siloed Models to System-Wide Intelligence

The real promise of foundation models isn’t just breadth, it’s continuity across the care pathway.

These models can analyze multiple pathologies, modalities and data types in a single pass, but they only become transformative when deployed inside a platform that provides the infrastructure to scale those insights across systems, specialties and settings.

That’s what the future of enterprise AI really means:

  • Full clinical coverage with context 
  • Ingesting data from across the system
  • Routing insights to the right teams, at the right time
  • Driving action inside existing workflows
  • Measuring what’s working and where

With the right foundation model running on the right platform, health systems can support enterprise-level care from a single, shared infrastructure.

Why Foundation Models Still Need a Platform

Even the most powerful model is only as useful as the system that supports it. Foundation models must be deployed within platforms that deliver insight at the point of care, enforce regulatory and quality safeguards and scale governance across departments and facilities.

The model-by-model era of “enterprise” AI is fading. The future belongs to platforms plus foundation models. Health systems don’t have to choose between today’s value and tomorrow’s potential. 

With the right platform, they can get both.

  1. Aidoc data on file.

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Andy Pollen
Andy Pollen is an experienced healthcare communicator and strategist who currently serves as the Director of Marketing Communications for Aidoc. Previously, he was the global marketing communications lead for critical care solutions within 3M Health Care's Medical Solutions Division, now Solventum. Pollen has also held communications positions with the University of Minnesota Academic Health Center, Indiana University Health and several business functions within Eli Lilly and Company through Borshoff, a creative services agency. He earned a bachelor’s degree in public relations and journalism from Ball State University and holds a master’s degree in business administration from Anderson University.
Andy Pollen
Director, Marketing Communications

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