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Related Concept Videos

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Issues And Trends In Healthcare Delivery System

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The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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Related Experiment Video

Updated: Dec 27, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Artificial intelligence as the next step towards precision pathology.

B Acs1, M Rantalainen2, J Hartman1

  • 1From the, Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden.

Journal of Internal Medicine
|March 5, 2020
PubMed
Summary

Machine learning and deep learning are revolutionizing diagnostic pathology. These computational pathology tools enhance cancer diagnosis accuracy, biomarker assessment, and prognostic predictions from digital slides.

Keywords:
artificial intelligencedeep learningdigital image analysisdigital pathologymachine learningpathology

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Area of Science:

  • Computational pathology
  • Digital pathology
  • Artificial intelligence in medicine

Background:

  • Accurate histopathologic diagnosis is crucial for personalized cancer therapy and biomarker assessment.
  • Digital image analysis and machine learning (ML) offer advancements in histomorphological evaluation.
  • Computational pathology, particularly deep learning, is rapidly advancing diagnostic capabilities.

Purpose of the Study:

  • To review the latest developments in digital image analysis for pathology.
  • To summarize the applications of artificial intelligence (AI) in diagnostic pathology.
  • To highlight ML's role in improving cancer diagnosis and prognosis.

Main Methods:

  • Review of recent literature on digital pathology and AI applications.
  • Analysis of machine learning and deep learning models in histopathology.
  • Summarization of AI's impact on diagnostic accuracy and biomarker assessment.

Main Results:

  • ML significantly improves metastases detection, Ki67 scoring, Gleason grading, and TIL scoring.
  • Deep learning models predict molecular markers from standard HE slides in various cancers.
  • Prognostic deep neural network models demonstrate predictive capabilities for survival outcomes.

Conclusions:

  • AI and digital pathology represent a significant milestone in healthcare.
  • ML applications enhance accuracy and efficiency in cancer diagnosis and treatment.
  • Future integration of AI into routine pathology practice is anticipated.