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Updated: Nov 21, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Published on: July 11, 2025

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Artificial intelligence and computational pathology.

Miao Cui1, David Y Zhang2

  • 1St. Luke's Roosevelt Hospital Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10025, USA.

Laboratory Investigation; a Journal of Technical Methods and Pathology
|January 17, 2021
PubMed
Summary
This summary is machine-generated.

Computational pathology integrates diverse data for advanced medical diagnostics. Challenges remain in data integration, hardware, and training, but it promises personalized patient care and improved workflows.

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Area of Science:

  • Pathology and Laboratory Medicine
  • Clinical Informatics
  • Computational Pathology

Background:

  • Data processing and machine learning are revolutionizing medicine, particularly pathology.
  • Clinical informatics, including genomics and bioinformatics, offers innovative patient care approaches.
  • Computational pathology is emerging to integrate whole-slide images, multi-omics data, and clinical informatics.

Purpose of the Study:

  • To review the clinical perspectives of computational pathology.
  • To discuss statistical methods, applications, obstacles, and future directions.
  • To highlight the potential of computational pathology in transforming patient care.

Main Methods:

  • Review of current literature and clinical practices in computational pathology.
  • Discussion of data integration challenges (multi-omics, imaging, clinical data).
  • Analysis of infrastructure needs (local labs, scan centers, cloud hubs).

Main Results:

  • Computational pathology faces hurdles in data integration, hardware, training, and ethical considerations.
  • Successful implementation requires changes in laboratory, scanning, and data processing infrastructure.
  • Potential benefits include improved workflow efficiency, diagnostic accuracy, and personalized medicine.

Conclusions:

  • Computational pathology offers a promising future for integrated diagnostics and personalized treatment.
  • Overcoming current challenges is crucial for realizing its full potential in healthcare.
  • Further development in data integration, infrastructure, and training is essential.