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

arXiv: https://arxiv.org/abs/2502.08333 · HTML: https://mlsanity.com/paper/2502.08333/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2025.

## Built on by (most cited first, 22 of 22)

- [PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology](https://mlsanity.com/paper/2505.20202/) (2025)
- [Multi-Modal Foundation Models for Computational Pathology: A Survey](https://mlsanity.com/paper/2503.09091/) (2025)
- [Scanner-Induced Domain Shifts Undermine the Robustness of Pathology Foundation Models](https://mlsanity.com/paper/2601.04163/) (2026)
- [Beyond the Failures: Rethinking Foundation Models in Pathology](https://mlsanity.com/paper/2510.23807/) (2025)
- [Pathryoshka: Compressing Pathology Foundation Models via Multi-Teacher Knowledge Distillation with Nested Embeddings](https://mlsanity.com/paper/2511.23204/) (2025)
- [LoC-Path: Learning to Compress for Pathology Multimodal Large Language Models](https://mlsanity.com/paper/2512.05391/) (2025)
- [Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges](https://mlsanity.com/paper/2505.10993/) (2025)
- [PS3: A Multimodal Transformer Integrating Pathology Reports with Histology Images and Biological Pathways for Cancer Survival Prediction](https://mlsanity.com/paper/2509.20022/) (2025)
- [Prototype-Guided Diffusion for Digital Pathology: Achieving Foundation Model Performance with Minimal Clinical Data](https://mlsanity.com/paper/2504.12351/) (2025)
- [Predicting Neoadjuvant Chemotherapy Response in Triple-Negative Breast Cancer Using Pre-Treatment Histopathologic Images](https://mlsanity.com/paper/2505.14730/) (2025)
- [Leveraging Foundation Models for Histological Grading in Cutaneous Squamous Cell Carcinoma using PathFMTools](https://mlsanity.com/paper/2511.19751/) (2025)
- [GloPath: An Entity-Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights](https://mlsanity.com/paper/2603.02926/) (2026)
- [Computational Pathology in the Era of Emerging Foundation and Agentic AI -- International Expert Perspectives on Clinical Integration and Translational Readiness](https://mlsanity.com/paper/2603.05884/) (2026)
- [From Classification to Cross-Modal Understanding: Leveraging Vision-Language Models for Fine-Grained Renal Pathology](https://mlsanity.com/paper/2511.11984/) (2025)
- [Benchmarking Pathology Foundation Models for Breast Cancer Survival Prediction](https://mlsanity.com/paper/2604.24679/) (2026)
- [Democratizing and accelerating AI-driven pathology research through agentic intelligence](https://mlsanity.com/paper/2606.20677/) (2026)
- [Comparing Computational Pathology Foundation Models using Representational Similarity Analysis](https://mlsanity.com/paper/2509.15482/) (2025)
- [PySlyde: A Lightweight, Open-Source Toolkit for Pathology Preprocessing](https://mlsanity.com/paper/2511.05183/) (2025)
- [Zero-shot segmentation of skin tumors in whole-slide images with vision-language foundation models](https://mlsanity.com/paper/2511.18978/) (2025)
- [ReconMIL: Synergizing Latent Space Reconstruction with Bi-Stream Mamba for Whole Slide Image Analysis](https://mlsanity.com/paper/2603.19925/) (2026)
- [Weakly Supervised Multicenter Nancy Index Scoring in Ulcerative Colitis Using Foundation Models](https://mlsanity.com/paper/2604.23706/) (2026)
- [EXPOSE: Explainable and Domain-Robust Embeddings from Pathology Vision Foundation Models using Sparse Autoencoders](https://mlsanity.com/paper/2608.28191/) (2026)

## Abstract (arXiv)

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.