242: Are Foundation Models Really Better for Digital Pathology? Podcast with Panu Kauppila

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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 background
  A workflow in which slides are scanned, analyzed automatically, and added to the worklist with results ready for review.
• 12:16 — Combining foundation models with curated annotations
  How smaller task-specific datasets and adapter technology can produce practical pathology models.
• 15:24 — Generalizability across scanners, laboratories, and populations
  How foundation models may make adaptation to new domains more manageable.
• 16:36 — Two datasets, two sources of potential bias
  The regulatory questions created by an underlying foundation model and a separate controlled annotated dataset.
• 20:46 — Making foundation models accessible
  Why a platform and user interface are necessary for pathologists and researchers who don’t work directly with code.
• 21:57 — Testing foundation models in Aiforia Create
  How researchers can compare a CNN with supported foundation models in the same no-code environment.
• 25:45 — How foundation models are selected
  Quality, licensing, model size, annotated data, and the requirements of the intended use.
• 26:57 — Training cost versus inference cost
  Why a more expensive training iteration may still reduce total development costs if fewer iterations are needed.
• 32:23 — Pathologists are visual reviewers
  The importance of segmentation quality and showing exactly what the model identified.
• 36:26 — The potential of multimodal AI
  Combining pathology images with text, genomic information, molecular data, and clinical outcomes.
• 37:35 — Keeping multimodal AI inside a controlled environment
  Privacy, security, regulatory oversight, and the risks of moving clinical information into consumer AI tools.
• 43:24 — Explainability in clinical pathology AI
  Using semantic segmentation, object detection, instance segmentation, and annotated ground truth to show how results were calculated.
• 47:43 — Moving toward predictive and prognostic models
  How established digital workflows could allow pathologists to contribute more information about likely outcomes.
• 49:45 — Research and clinical collaboration with Aiforia
  How interested researchers and laboratories can connect through the Aiforia website.

Resources Mentioned

• Aiforia
• Aiforia Create no-code model-development environment
• PathChat

Listen to the full discussion to understand what foundation models can add to digital pathology—and what still has to happen before they become practical, trusted clinical tools.

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