Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact

2025-02-12

AI Summary

Generative and multimodal foundation models are transforming computational pathology’s clinical diagnostic paradigms, yet lack standardized definitions of “foundationality” and clinically aligned evaluation criteria. Method: We conduct a systematic review of their evolution in multi-scale tissue image understanding, generative diagnostic reporting, and clinical question answering. We propose the first domain-specific definition of foundationality for pathology models and introduce a four-dimensional clinical adoption framework—covering data, model architecture, evaluation protocols, and deployment readiness—with emphasis on trustworthiness, interpretability, and societal acceptability. Our approach integrates self-supervised vision models, vision-language contrastive learning, and generative AI collaborators, optimized for gigapixel whole-slide image analysis. Contribution/Results: We advocate for cross-institutional benchmark development and regulatory validation to accelerate real-world clinical translation, establishing a roadmap for trustworthy, scalable, and deployable foundation models in digital pathology.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Other Foundations of Computer Vision
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Data transparency and provenance

Abstract

From self-supervised, vision-only models to contrastive visual-language frameworks, computational pathology has rapidly evolved in recent years. Generative AI"co-pilots"now demonstrate the ability to mine subtle, sub-visual tissue cues across the cellular-to-pathology spectrum, generate comprehensive reports, and respond to complex user queries. The scale of data has surged dramatically, growing from tens to millions of multi-gigapixel tissue images, while the number of trainable parameters in these models has risen to several billion. The critical question remains: how will this new wave of generative and multi-purpose AI transform clinical diagnostics? In this article, we explore the true potential of these innovations and their integration into clinical practice. We review the rapid progress of foundation models in pathology, clarify their applications and significance. More precisely, we examine the very definition of foundational models, identifying what makes them foundational, general, or multipurpose, and assess their impact on computational pathology. Additionally, we address the unique challenges associated with their development and evaluation. These models have demonstrated exceptional predictive and generative capabilities, but establishing global benchmarks is crucial to enhancing evaluation standards and fostering their widespread clinical adoption. In computational pathology, the broader impact of frontier AI ultimately depends on widespread adoption and societal acceptance. While direct public exposure is not strictly necessary, it remains a powerful tool for dispelling misconceptions, building trust, and securing regulatory support.

Problem

• Evaluate impact of generative AI in clinical diagnostics
• Assess foundational models' role in pathology
• Establish global benchmarks for AI evaluation

Innovation

• Generative AI co-pilots
• Contrastive visual-language frameworks
• Multi-gigapixel tissue images analysis