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Computational pathology in precision oncology: Evolution from task-specific models to foundation models.

Yuhao Wang1,2, Yunjie Gu1,2, Xueyuan Zhang3

  • 1School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Anhui, Hefei 230026, China.

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|September 26, 2025
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Summary
This summary is machine-generated.

Foundation models (FMs) are revolutionizing computational pathology by moving beyond task-specific AI. These advanced models offer scalable solutions for diverse clinical tasks, including rare diseases and biomarker discovery in oncology.

Keywords:
Artificial intelligenceComputational pathologyDeep learningFoundation modelsPrecision oncology

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Area of Science:

  • Computational pathology
  • Artificial intelligence in medicine
  • Biomedical informatics

Background:

  • Artificial intelligence (AI) is increasingly integrated into clinical pathology workflows, improving accuracy and efficiency.
  • Traditional AI models require extensive labeled datasets for specific tasks, limiting scalability for rare diseases and open-set identification.
  • The need for adaptable AI solutions necessitates a shift from task-specific models to more versatile foundation models (FMs).

Purpose of the Study:

  • To review recent advancements in pathological foundation models (FMs).
  • To categorize FMs based on their architecture and functionalities (image, image-text, image-gene).
  • To explore the applications, challenges, and opportunities of FMs in precision oncology.

Main Methods:

  • Literature review of recent research on pathological foundation models.
  • Classification of FMs into three main categories: pathology image FMs, pathology image-text FMs, and pathology image-gene FMs.
  • Analysis of the application scenarios and functionalities of each FM category, particularly in oncology.

Main Results:

  • Pathological FMs have emerged as a powerful tool, categorized into image, image-text, and image-gene types.
  • These FMs demonstrate diverse functionalities and application scenarios, enhancing diagnosis, treatment, prognosis, and biomarker discovery.
  • Significant progress has been made in applying FMs to oncology, paving the way for precision medicine.

Conclusions:

  • Foundation models represent a paradigm shift in computational pathology, addressing limitations of task-specific approaches.
  • Pathological FMs offer scalable and adaptable solutions for a wide range of clinical tasks, including rare disease analysis.
  • FMs hold immense potential for advancing precision oncology by enabling more sophisticated biomarker discovery and personalized treatment strategies.