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Updated: May 13, 2026

Mast Cells in the Microenvironment of Hepatocellular Carcinoma Confer Favorable Prognosis: A Retrospective Study using QuPath Image Analysis Software

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Foundation models in computational pathology: methods, applications and clinical implications.

Rasoul Sali¹, Yodit Aschenaki¹, Raymond Leveillee^2,3

¹Computational Pathology, Novino AI, Fort Lauderdale, Florida, USA.

|May 12, 2026

Summary

This summary is machine-generated.

Pathology foundation AI models offer generalizable cancer diagnosis and precision oncology solutions. These advanced artificial intelligence systems aim to improve clinical translation and workflow integration in pathology.

Keywords:

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Area of Science:

Digital pathology
Artificial intelligence (AI)
Oncological pathology

Background:

Digitized histopathology has spurred AI applications in cancer diagnosis and precision oncology.
Current AI systems are often task-specific and lack generalizability across clinical settings.
Pathology foundation AI models represent a paradigm shift towards generalizable representations of tissue morphology.

Purpose of the Study:

To review the development, methodology, and current landscape of pathology foundation models in oncological pathology.
To outline the evolution, trends, and classification of major pathology foundation model types and modalities.
To evaluate the capabilities of foundation models compared to conventional AI and explore the transition to agentic AI systems.

Main Methods:

Narrative review of pathology foundation models.
Analysis of model evolution, trends, and classification.
Evaluation of model capabilities, advantages, and regulatory frameworks.

Main Results:

Foundation AI models enable learning generalizable representations for diverse downstream tasks.
These models offer advantages over narrowly task-specific AI systems in clinical translation.
The review covers model types, modalities, and the transition towards agentic AI.

Conclusions:

Pathology foundation models are crucial for advancing AI in oncological pathology.
These models promise improved generalizability, clinical translation, and workflow integration.
Regulatory and governance frameworks are essential for responsible AI implementation in pathology.