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Revolutionizing Digital Pathology With the Power of Generative Artificial Intelligence and Foundation Models.

Asim Waqas1, Marilyn M Bui2, Eric F Glassy3

  • 1Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida; Department of Electrical Engineering, University of South Florida, Tampa, Florida.

Laboratory Investigation; a Journal of Technical Methods and Pathology
|September 27, 2023
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Summary

Foundation models and generative AI offer transformative potential for digital pathology. These advanced AI systems can overcome limitations of task-specific models, improving efficiency and objectivity in tasks like image analysis and diagnosis.

Keywords:
artificial intelligencecomputational and digital pathologyfoundation modelslarge language modelsmultimodal datavision-language models

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Area of Science:

  • Computational pathology
  • Artificial intelligence in medicine
  • Digital pathology workflows

Background:

  • Digital pathology utilizes whole-slide imaging for computer-assisted analysis, enabling computational pathology.
  • Task-specific AI/ML models, like CNNs, show high performance but require extensive annotated data and lack generalization.
  • Limitations include data dependency, inability to use multimodal data, and poor generalization to new datasets or variations.

Purpose of the Study:

  • To review advances in computational pathology driven by task-specific AI and their challenges.
  • To introduce foundation models and generative AI as solutions to current AI limitations in pathology.
  • To propose a pathology-specific generative AI based on multimodal foundation models for transformative applications.

Main Methods:

  • Review of recent advancements in task-specific AI for computational pathology.
  • Introduction and explanation of foundation models and generative AI concepts.
  • Proposal for a multimodal foundation model-based generative AI tailored for pathology.

Main Results:

  • Task-specific AI/ML models face challenges with data annotation, multimodal data integration, and generalization.
  • Foundation models offer in-context learning, self-correction, and adaptability with less annotated data.
  • Generative AI based on multimodal foundation models can enhance objectivity and efficiency in pathology tasks.

Conclusions:

  • Generative AI, particularly pathology-specific models from multimodal foundation models, holds significant promise for digital pathology.
  • These AI systems can serve as expert companions for pathologists, aiding in image analysis, report generation, diagnosis, and prognosis.
  • Foundation models and generative AI can standardize pathology workflows, education, and training.