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Updated: May 28, 2025

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Pathology Foundation Models.

Mieko Ochi1, Daisuke Komura1, Shumpei Ishikawa1

  • 1Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

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|February 10, 2025
PubMed
Summary
This summary is machine-generated.

Foundation models (FMs) in pathology AI enhance disease diagnosis and treatment planning. Ongoing research addresses challenges for broader clinical integration and personalized medicine.

Keywords:
artificial intelligencefoundation modelgeneralist medical AImedicinepathology

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Area of Science:

  • Digital pathology
  • Artificial intelligence in medicine
  • Computational pathology

Background:

  • Pathology is vital for diagnosing diseases using tissue samples from surgeries and biopsies.
  • Whole slide imaging and deep learning have significantly advanced pathology AI.
  • Foundation models (FMs) represent a new generation of AI, offering improved accuracy and versatility.

Purpose of the Study:

  • To review the current applications of foundation models (FMs) in pathology.
  • To highlight the potential of FMs in various diagnostic and prognostic tasks.
  • To discuss the challenges and future directions for FMs in clinical pathology.

Main Methods:

  • Review of recent literature on foundation models in pathology.
  • Analysis of reported applications in disease diagnosis, prognosis, and biomarker assessment.
  • Discussion of challenges in clinical implementation and future research trends.

Main Results:

  • Pathology FMs show promise in diagnosing diseases, predicting patient survival, and assessing biomarkers.
  • Applications include rare cancer detection and immunohistochemical scoring.
  • FMs offer enhanced accuracy and broader applicability compared to traditional AI models.

Conclusions:

  • Foundation models are transforming pathology AI, aiding pathologists and improving patient care.
  • Addressing current challenges is crucial for widespread clinical adoption.
  • Future integration of pathology FMs with other medical AI domains will drive precision medicine.