Industries GUIDE
AI in Pathology
Computational pathology applies image-analysis methods to digitized tissue slides.
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Overview
Whole-slide images are large, so models often process patches and combine patch-level evidence for slide-level tasks.
Deep Dive
The FDA De Novo review for Paige Prostate describes a computer-assisted pathology device that identifies suspicious areas on digitized prostate biopsy slides for pathologist review. CLAM is a research approach using attention-based multiple-instance learning for whole-slide classification with slide-level labels. Ki-67 is a cell-proliferation marker discussed in oncology. These tools and markers address specific tasks; their research or regulatory status does not generalize to every tissue, cancer or clinical decision.
Real-World Implementation
A whole-slide image can be divided into tiles for model processing.
Multiple-instance learning can learn from slide-level labels when every patch is not individually annotated.
The FDA-reviewed Paige Prostate device assists pathologists with a specific prostate-biopsy review task.
Ki-67 is a protein marker used in assessing cell proliferation; an AI count is a measurement aid, not a standalone cancer diagnosis.
The Future of AI in Pathology
Report the tissue type, scanner, population, reference standard and intended use when evaluating pathology AI. Keep pathologist oversight and assess performance across sites and specimen types. A clearance or benchmark for one device does not establish general clinical utility across pathology.
Putting it into practice
Why it matters
- Context and rules. Industry context determines whether AI ideas survive contact with reality.
- Quality control. Domain constraints influence acceptable error rates and oversight models.
- Build choices. Successful deployments align technical capability with frontline workflows.
Watch out for
- Regulatory requirements can invalidate otherwise strong prototypes.
- Historical data may encode bias that harms specific communities.
- Legacy systems can create integration bottlenecks and hidden costs.
Next steps
- Involve domain experts from problem framing to evaluation.
- Design audit trails and documentation before launch.
- Validate compliance and safety obligations early.
- Roll out in phases with clear stop and rollback criteria.
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Frequently asked questions
What is AI in Pathology?
Computational pathology applies image-analysis methods to digitized tissue slides. Whole-slide images are large, so models often process patches and combine patch-level evidence for slide-level tasks.
What is a 'whole-slide image' in digital pathology?
Slides are scanned into enormous gigapixel images, often billions of pixels, that AI then analyzes.
Why do AI models process slides in small tiles or patches?
Whole-slide images are far too big for a model's input, so they are split into thousands of tiles and results are recombined.
What regulatory pathway did FDA use for Paige Prostate?
FDA’s DEN200080 record is a De Novo classification for a specific software device and intended use; the decision does not generalize to all pathology AI.
What does an AI Ki-67 count measure in a tumor?
Ki-67 is a marker of cell proliferation, and counting positive cells indicates how aggressively a tumor is growing.
What can an AI system provide in a pathology workflow, according to the scoped examples?
The cited FDA review describes a device intended to assist pathologists; its output is not a standalone diagnosis or treatment decision.
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