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Computational pathology: an evolving concept

  • Ioannis Prassas , Blaise Clarke , Timothy Youssef , Juliana Phlamon , Lampros Dimitrakopoulos , Andrew Rofaeil and George M. Yousef EMAIL logo
Published/Copyright: April 23, 2024
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Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 62 Issue 11

Abstract

The initial enthusiasm about computational pathology (CP) and artificial intelligence (AI) was that they will replace pathologists entirely on the way to fully automated diagnostics. It is becoming clear that currently this is not the immediate model to pursue. On top of the legal and regulatory complexities surrounding its implementation, the majority of tested machine learning (ML)-based predictive algorithms do not display the exquisite performance needed to render them unequivocal, standalone decision makers for matters with direct implications to human health. We are thus moving into a different model of “computer-assisted diagnostics”, where AI is there to provide support, rather than replacing, the pathologist. Herein we focus on the practical aspects of CP, from a pathologist perspective. There is a wide range of potential applications where CP can enhance precision of pathology diagnosis, tailor prognostic and predictive information, as well as save time. There are, however, a number of potential limitations for CP that currently hinder their wider adoption in the clinical setting. We address the key necessary steps towards clinical implementation of computational pathology, discuss the significant obstacles that hinders its adoption in the clinical context and summarize some proposed solutions. We conclude that the advancement of CP in the clinic is a promising resource-intensive endeavour that requires broad and inclusive collaborations between academia, industry, and regulatory bodies.

Keywords: digital pathology; computational pathology; AI in healthcare; machine learning
Corresponding author: George M. Yousef, MD, PhD, FRCPC (Path), Laboratory Medicine Program, 7989 University Health Network , 200 Elizabeth Str., Toronto, ON, M5G 2C4, Canada; and Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada, Phone: +416 340-4800 x 3686, Fax: +416 586-1426, E-mail:

Acknowledgments

We would like to help Bishoy Samaan for his helfpul discussions on the preparation of this Mini-Review.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission. IP and GMY drafted this Mini-Review. BC and TY edited the manuscript. JP and RG worked on the Figures/References and edited the manuscript. (1) Conceptualization: GMY and IP came up with the idea for this mini-review. (2) Literature review: IP, GMY, TY and AR conducted the literature review. (3) Writing: IP, GMY and BC were involved in drafting the manuscript. (4) Critical review and editing: BC conducted the critical review of the manuscript. (5) Figures and tables: JP was responsible for creating figures. (6) Supervision: IP, GMY supervised the overall progress of the mini-review.

  4. Competing interests: The authors state no conflict of interest.

  5. Research funding: None declared.

  6. Data availability: Not applicable.

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Received: 2023-10-24
Accepted: 2024-04-10
Published Online: 2024-04-23
Published in Print: 2024-10-28

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/cclm-2023-1124
Keywords for this article
digital pathology; computational pathology; AI in healthcare; machine learning

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Circulating tumor DNA measurement: a new pillar of medical oncology?
  4. Reviews
  5. Circulating tumor DNA: current implementation issues and future challenges for clinical utility
  6. Circulating tumor DNA methylation: a promising clinical tool for cancer diagnosis and management
  7. Opinion Papers
  8. The final part of the CRESS trilogy – how to evaluate the quality of stability studies
  9. The impact of physiological variations on personalized reference intervals and decision limits: an in-depth analysis
  10. Computational pathology: an evolving concept
  11. Perspectives
  12. Dynamic mirroring: unveiling the role of digital twins, artificial intelligence and synthetic data for personalized medicine in laboratory medicine
  13. General Clinical Chemistry and Laboratory Medicine
  14. Macroprolactin in mothers and their babies: what is its origin?
  15. The influence of undetected hemolysis on POCT potassium results in the emergency department
  16. Quality control in the Netherlands; todays practices and starting points for guidance and future research
  17. QC Constellation: a cutting-edge solution for risk and patient-based quality control in clinical laboratories
  18. OILVEQ: an Italian external quality control scheme for cannabinoids analysis in galenic preparations of cannabis oil
  19. Using Bland-Altman plot-based harmonization algorithm to optimize the harmonization for immunoassays
  20. Comparison of a two-step Tempus600 hub solution single-tube vs. container-based, one-step pneumatic transport system
  21. Evaluating the HYDRASHIFT 2/4 Daratumumab assay: a powerful approach to assess treatment response in multiple myeloma
  22. Insight into the status of plasma renin and aldosterone measurement: findings from 526 clinical laboratories in China
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  24. Reference values for plasma and urine trace elements in a Swiss population-based cohort
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  26. Within- and between-subject biological variation estimates for the enumeration of lymphocyte deep immunophenotyping and monocyte subsets
  27. Diurnal and day-to-day biological variation of salivary cortisol and cortisone
  28. Web-accessible critical limits and critical values for urgent clinician notification
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  30. Thyroglobulin measurement is the most powerful outcome predictor in differentiated thyroid cancer: a decision tree analysis in a European multicenter series
  31. Cardiovascular Diseases
  32. Interaction of heparin with human cardiac troponin complex and its influence on the immunodetection of troponins in human blood samples
  33. Diagnostic performance of a point of care high-sensitivity cardiac troponin I assay and single measurement evaluation to rule out and rule in acute coronary syndrome
  34. Corrigendum
  35. Reference intervals of 24 trace elements in blood, plasma and erythrocytes for the Slovenian adult population
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