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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Artificial intelligence for solid tumour diagnosis in digital pathology.

Christophe Klein1, Qinghe Zeng1,2, Floriane Arbaretaz1

  • 1Centre d'Histologie, d'Imagerie et de Cytométrie (CHIC), Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Paris, France.

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Digital pathology uses AI to analyze digitized histology slides, improving tumour diagnosis. This review explores AI

Keywords:
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Area of Science:

  • Histopathology
  • Digital Pathology
  • Artificial Intelligence in Medicine

Background:

  • Traditional tumour diagnosis relies on manual microscopic examination of histological slides.
  • Digital pathology enables quantitative analysis of slides through image analysis.
  • Advancements in artificial intelligence (AI) have significantly impacted medical image analysis.

Purpose of the Study:

  • To review the technique of digital pathology.
  • To present recent AI applications in tumour histopathology.
  • To discuss the future integration of AI into routine histopathology.

Main Methods:

  • Review of current literature on digital pathology and AI in histopathology.
  • Illustration of AI-assisted pathology techniques.
  • Presentation of case studies and examples of AI applications.

Main Results:

  • AI algorithms show feasibility and usefulness in pathology tasks.
  • Digital pathology combined with AI offers quantitative insights.
  • AI is increasingly being applied to analyze histological slides.

Conclusions:

  • AI-assisted digital pathology is a rapidly advancing field.
  • AI holds significant potential to enhance tumour diagnosis and histopathology workflows.
  • Future integration of AI into routine practice is anticipated.