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What good is a powerful foundation model if it slows the pathologist down, can’t explain its result, or doesn’t fit the clinical workflow?

Foundation models are gaining attention across digital pathology. But they’re not finished clinical tools by themselves.

In this episode of the Digital Pathology Podcast, I speak with Panu Kauppila, Chief Product Officer at Aiforia, about what foundation models are, how they differ from traditional convolutional neural networks, and what it takes to make them useful for pathologists.

Panu describes a foundation model as a large, context-aware building block. To perform a specific pathology task - such as grading, segmentation, or mitotic counting - it must be combined with an adapter, a task-specific head, curated annotations, a usable interface, and integration with the laboratory workflow.

We also discuss one of the biggest practical constraints: speed.

A pathologist shouldn’t have to click a button and wait for an analysis. Panu explains why AI should run automatically in the background so the results are already available when the case reaches the pathologist’s worklist.

The conversation also examines the tradeoff between model size, computational cost, and clinical performance. Larger models may improve robustness and generalizability, but they can also require more processing power. For clinical applications, Aiforia focuses on smaller and medium-sized foundation models that provide the necessary quality without making the workflow slower or unnecessarily expensive.

Annotated data remains central. The foundation model supplies the underlying image understanding, while the task-specific head and controlled annotations determine how the model performs on a particular pathology problem. This structure also raises important questions about bias, data provenance, ownership, regulatory documentation, and explainability.

Finally, we look at multimodal AI models that combine pathology images with reports, genomic data, molecular information, and clinical outcomes. These tools could support more interactive, predictive, and prognostic applications - but only if they’re introduced through secure, controlled workflows with clear audit trails.

Episode Highlights

• 00:00 — Where does bias enter a foundation model workflow?
  Panu distinguishes the underlying foundation model from the annotated dataset used to build the task-specific application.
• 00:27 — Meet Panu Kauppila
  An introduction to Aiforia’s Chief Product Officer and the episode’s focus on foundation models in digital pathology.
• 01:05 — From radiology AI to digital pathology
  Panu describes his background in medical device development, radiology, oncology solutions, and clinical AI implementation.
• 06:29 — Foundation models versus convolutional neural networks
  What makes foundation models more context-aware, robust, and generalizable across image datasets.
• 07:07 — Image-only and multimodal foundation models
  Why these two categories offer different capabilities and potential clinical uses.
• 07:50 — A foundation model is a platform, not a finished solution
  The underlying model may understand image features, but it still needs a task, interface, and clinical workflow.
• 08:34 — Foundation model, adapter, and task-specific head
  How these components work together to create an application for grading, mitotic counting, or another pathology task.
• 09:57 — The cost of larger models
  Why increased robustness must be balanced against computational demands, inference speed, and affordability.
• 11:12 — Pathologists won’t wait for AI
  Why even short delays can interrupt the clinical workflow.
• 11:46 — Running AI in the