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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Introduction to digital pathology and computer-aided pathology.

Soojeong Nam1, Yosep Chong2, Chan Kwon Jung2

  • 1Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.

Journal of Pathology and Translational Medicine
|February 13, 2020
PubMed
Summary
This summary is machine-generated.

Digital pathology (DP) and computer-aided pathology (CAP) offer diagnostic support but face challenges. Understanding DP's technical aspects and addressing implementation hurdles are crucial for pathologist adoption.

Keywords:
Artificial intelligenceComputer-aided pathologyDeep learningDigital pathology

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Area of Science:

  • Pathology
  • Medical Informatics
  • Computer Science

Background:

  • Digital pathology (DP) is increasingly recognized but its underlying engineering and mathematical principles remain challenging for many pathologists.
  • Computer-aided pathology (CAP) tools assist in diagnosis, yet concerns persist regarding their impact on the pathology profession and clinical validity.
  • Implementing DP presents significant burdens, requiring careful consideration of technical factors, workflow integration, and IT infrastructure.

Purpose of the Study:

  • To define key terms in digital pathology and computer-aided pathologic diagnosis.
  • To review current applications and challenges associated with digital pathology implementation.
  • To discuss the development and limitations of computer-aided diagnostic tools.

Main Methods:

  • Literature review and synthesis of current knowledge on digital pathology and computer-aided pathology.
  • Definition of essential terminology for clarity and understanding.
  • Analysis of implementation challenges, including technical, workflow, and IT aspects.

Main Results:

  • Digital pathology and computer-aided pathology are evolving fields with significant diagnostic potential.
  • Understanding the technical underpinnings of DP and CAP is essential for effective adoption.
  • Implementation barriers include technical complexity, workflow disruption, and IT infrastructure requirements.
  • Existing CAP tools have limitations that need further development and validation.

Conclusions:

  • Digital pathology and computer-aided pathology offer valuable tools for pathologists, but require a deeper understanding of their technical foundations.
  • Addressing pathologist concerns about job security and clinical utility is vital for widespread acceptance.
  • Successful implementation necessitates strategic planning for technical integration, workflow adaptation, and robust IT support.
  • Continued research and development are needed to enhance the capabilities and overcome the limitations of computer-aided diagnostic tools.