Unleashing the potential of AI for pathology: challenges and recommendations

Amina Asif, Kashif Rajpoot, Simon Graham, David Snead, Fayyaz Minhas, Nasir Rajpoot*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

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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.
Original languageEnglish
JournalJournal of Pathology
Early online date7 Aug 2023
DOIs
Publication statusE-pub ahead of print - 7 Aug 2023

Keywords

  • computational pathology
  • whole slide images
  • deep learning
  • histopathology
  • machine learning
  • artificial intelligence

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