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Review
. 2023 Aug;260(5):564-577.
doi: 10.1002/path.6168. Epub 2023 Aug 7.

Unleashing the potential of AI for pathology: challenges and recommendations

Amina Asif  1 , Kashif Rajpoot  2 , Simon Graham  3 , David Snead  3   4 , Fayyaz Minhas  1   5 , Nasir Rajpoot  1   3   5   6
Affiliations
Review

Unleashing the potential of AI for pathology: challenges and recommendations

Amina Asif et al. J Pathol. 2023 Aug.

Abstract

Computational pathology is currently witnessing a surge in the development of AI techniques, offering promise for achieving breakthroughs and significantly impacting the practices of pathology and oncology. These AI methods bring with them the potential to revolutionize diagnostic pipelines as well as treatment planning and overall patient care. Numerous peer-reviewed studies reporting remarkable performance across diverse tasks serve as a testimony to the potential of AI in the field. However, widespread adoption of these methods in clinical and pre-clinical settings still remains a challenge. In this review article, we present a detailed analysis of the major obstacles encountered during the development of effective models and their deployment in practice. We aim to provide readers with an overview of the latest developments, assist them with insights into identifying some specific challenges that may require resolution, and suggest recommendations and potential future research directions. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Keywords: artificial intelligence; computational pathology; deep learning; histopathology; machine learning; whole slide images.

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Figures

Figure 1
Figure 1
Number of research publications in AI‐based computational pathology recorded in PubMed over the last decade.
Figure 2
Figure 2
The conventional workflow followed in the development of a CPath system and challenges associated with each phase. The figure has been created using lucidchart.com. Publicly available icons from flaticon.com have been used in the figure.
Figure 3
Figure 3
Categorization of CPath methods based on the level of analysis. The figure has been created using lucidchart.com. Publicly available icons from flaticon.com have been used in the figure.
Figure 4
Figure 4
Complexity of CPath tasks in terms of their mundanity. Publicly available icons from flaticon.com and icons8.com have been used in the figure.
See this image and copyright information in PMC

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