ArticleNature communications2026
Towards robust foundation models for digital pathology.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- PathScaleBench: A Multidimensional Benchmark of Cross-Scale Representation Behavior and Magnification-Shift Robustness in Pathology Foundation Models.Journal of imaging informatics in medicine · 2026Article
- End-to-end multimodal pathology foundation model with clinical dialogue.Nature medicine · 2026Article
- Foundation Models for Liquid Chromatography-High-Resolution Mass Spectrometry: A New Era beyond Labeled Datasets.Analytical chemistry · 2026Article
- A protocol for evaluating robustness to H&E staining variation in computational pathology models.Journal of pathology informatics · 2026Article
- Towards robust foundation models for digital pathology.Nature communications · 2026Article
- An evaluation of pathology foundation models with weakly supervised learning for BRAF mutation prediction in melanoma whole slide images.Frontiers in medical technology · 2026Article
- Foundation Models in Cancer Pathology: Techniques, Applications, and Future Directions.Research (Washington, D.C.) · 2026Review
- Reproducibility of digital pathology features extracted from deep learning and foundational AI models on sequential tissue slides.Scientific reports · 2025Article
- A multimodal whole-slide foundation model for pathology.Nature medicine · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Biomedical Foundation Models (FMs) are transforming AI-enabled healthcare research and entering clinical validation. However, their susceptibility to learning non-biological features - including variations in laboratory procedures and scanner hardware - poses risks for clinical deployment. We introduce PathoROB, a public benchmark quantifying FM robustness to non-biological features. Representation-level robustness is assessed using the robustness index, while output-level robustness is evaluated across clinically relevant settings, including patch- and slide-level prediction, case retrieval, and clustering tasks. Our experiments reveal robustness deficits across all 20 evaluated FMs, with substantial differences between them. We find that non-robust FM representations can cause major diagnostic downstream errors preventing safe clinical adoption. Using more robust FMs, vision-language alignment, and post-hoc robustification reduces (but does not yet eliminate) the risk of such errors. This work establishes that robustness evaluation is essential for validating pathology FMs before clinical adoption and provides a blueprint for assessing and improving robustness across biomedical domains.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.