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Blog
Andy Pollen, Sasha Eizenberg

Foundation Models 101: Why the Future of Clinical AI Starts Here

aiOS, Data Management, Deep Learning, Foundation Models
Table of Contents

In the last decade, AI in healthcare has evolved rapidly but unevenly. 

While we’ve seen remarkable breakthroughs in imaging AI and decision support tools, the underlying approach to development has remained fundamentally narrow: one model, one task and one department at a time.

That approach is no longer sufficient.

As clinical demands increase and data complexity grows, the real challenge isn’t just building more models, it’s building the model that lets us scale clinical AI across more conditions, more departments and more workflows. 

That’s where foundation models come in.

The Problem with Traditional AI: High Effort, Limited Scope

To understand why foundation models matter, we need to examine how most clinical AI has been built to date.

The standard workflow is familiar:

  • A model is trained on thousands, sometimes hundreds of thousands, of manually labeled examples.
  • It learns to perform one task through trial and error — say, identifying suspected  pulmonary embolism (PE) on a CT. 
  • If you want to solve another problem, you start over.

This creates a bottleneck. Each new use case requires:

  • A massive labeled dataset
  • Significant development time
  • Domain-specific validation and deployment

This results in high costs, long timelines and fragmented systems that struggle to keep pace with clinical needs.

Foundation Models Offer A New Architecture for Scale

Foundation models offer a fundamentally different approach. Rather than learning one task at a time, these models are trained to understand the structure of medical data itself across imaging, clinical text, electronic health records (EHRs) and more.

They use self-supervised learning — minimal labeling required — to build general-purpose representations of anatomy, pathology and clinical context. Once trained, these models become a launchpad for a wide range of downstream applications.

From a single foundation, you can fine-tune models to support dozens of clinical tasks today — with the potential for full diagnostic coverage ahead. This includes:

  • Triage and detection
  • Disease characterization and measurement
  • Follow-up tracking 
  • Risk stratification and care coordination
  • Report generation and clinical summarization
  • Historical comparison and longitudinal analysis

And you can do it in weeks, not years.

Why It Matters Now

The rise of foundation models couldn’t come at a more critical moment. Clinical complexity has outpaced the capacity of even the best-trained teams. Clinicians aren’t overwhelmed because they lack expertise. They’re overwhelmed because the tools meant to assist have created more bottlenecks.

Foundation models change the equation. They enable:

  • Speed: Dramatically faster AI training and deployment
  • Scale: Broader coverage across service lines and use cases
  • Transformation: Intelligence that spans the care continuum

This isn’t just about making AI better. It’s about making it work — reliably, efficiently and enterprise-wide.

Multimodality: Building a System That Understands Context

Foundation models embrace multimodality, which is the ability to learn across diverse data types: imaging, text, labs, vitals and structured clinical records.

This is how clinicians think. AI should do the same.

By linking radiology reports to images, patient histories to findings and lab values to clinical trends, multimodal foundation models move beyond recognition to reasoning.

They don’t just answer “what’s on this scan?” They begin to answer “what does this mean for this patient, right now?”

This is the path to real clinical intelligence.

From Algorithms to Infrastructure

The most advanced clinical AI systems today cover perhaps 20–30 use cases, but there are over 70,000 ICD-10 codes. Yet, we’re still solving problems one algorithm at a time.

To break through, we need AI that is:

  • Comprehensive: Not limited to high-volume use cases
  • Flexible: Adaptable to new challenges without retraining from scratch
  • Context-aware: Capable of integrating the full spectrum of patient data

Foundation models are the first technology to deliver all three.

This isn’t a future vision — it’s already happening. 

At Aidoc, we’ve already begun deploying foundation model-based solutions with plans to scale to hundreds more over the next few years. Our Clinical AI Reasoning Engine (CARE™) is built to harness multimodal data and drive proactive, intelligent care coordination.

What Comes Next

AI’s first wave brought us disease awareness support. The second brought workflow efficiency. Foundation models usher in the third: enterprise intelligence — a connected system that learns, reasons and acts in context.

The transition is already underway, but not every organization is ready. Without the right infrastructure, even the most advanced foundation models can’t scale. Real-world impact requires more than innovation; it requires a platform built to orchestrate, integrate, measure and govern that intelligence across the health system.

Those who embrace foundation models plus platforms aren’t just gaining access to more algorithms — they’re investing in a smarter, faster and more resilient health system. One where AI isn’t siloed to point solutions but serves as a connective layer across the continuum of care.

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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
Sasha Eizenberg
Sasha Eizenberg is the Director of Solution Launch at Aidoc, responsible for developing strategic how-to guides and training teams on the best practices for implementing Aidoc products at customer sites. With an master's degree in biochemistry and a career rooted in the medical device industry, she brings a unique blend of scientific expertise and strategic insight to her role. Eizenberg began her tenure at Aidoc in customer success, where she gained a deep understanding of customer challenges, preferences and needs. She's committed to aligning product strategies with customer expectations, ensuring the smooth and effective rollout of new products.
Sasha Eizenberg
Director, Solution Launch

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