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Digital Pathology: Advantages, Limitations and Emerging Perspectives.

Stephan W Jahn1, Markus Plass1, Farid Moinfar1,2

  • 1Diagnostic and Research Institute of Pathology, Medical University of Graz, Neue Stiftingtalstraße 6, 8010 Graz, Austria.

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Digital pathology offers routine diagnostics, but large-scale implementation faces technical and logistical hurdles. Training and addressing pathologist concerns are crucial for successful adoption of digital slides and AI tools.

Keywords:
artificial intelligenceautomationcomputer vision syndromedigital pathologymachine learningoccupational healthwhole slide imaging

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Area of Science:

  • Digital pathology
  • Computational pathology
  • Medical imaging

Background:

  • Digital pathology is emerging as a mainstream diagnostic tool, driven by advancements in whole slide imaging.
  • Large-scale implementation faces significant technical, logistical, and financial challenges.
  • Existing studies show safety and feasibility, but practical implementation highlights training needs and potential pitfalls.

Purpose of the Study:

  • To discuss experiences from past digital pathology implementations.
  • To explore future possibilities with artificial intelligence integration.
  • To address technical, occupational health, and professional challenges in digital pathology.

Main Methods:

  • Review of implementation experiences and comparative studies in digital pathology.
  • Analysis of international guidelines for digital histology.
  • Discussion of the integration of image analysis and artificial intelligence.

Main Results:

  • Pathologist reluctance to adopt digital slides (up to 50%) due to unfamiliarity and loss of traditional tools.
  • Need for comprehensive guidelines covering image acquisition, workstations, and archiving.
  • Emerging role of artificial intelligence challenging human evaluation as the gold standard.

Conclusions:

  • Successful digital pathology implementation requires addressing pathologist training, workflow integration, and occupational health.
  • International guidelines provide a foundation but require further development for cost-efficiency and health concerns.
  • The integration of AI in digital pathology necessitates a re-evaluation of the pathologist's role and traditional diagnostic standards.