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Current and future applications of artificial intelligence in pathology: a clinical perspective
  1. Emad A Rakha1,
  2. Michael Toss1,
  3. Sho Shiino1,
  4. Paul Gamble2,
  5. Ronnachai Jaroensri2,
  6. Craig H Mermel2,
  7. Po-Hsuan Cameron Chen2
  1. 1Histopathology, University of Nottingham School of Medicine, Nottingham, UK
  2. 2Google Health, Google, Palo Alto, California, USA
  1. Correspondence to Dr Emad A Rakha, Department of Histopathology, Nottingham University Hospital NHS Trust, City Hospital Campus, Hucknall Road, Nottingham, NG5 1PB, UK; emad.rakha{at}nottingham.ac.uk

Abstract

During the last decade, a dramatic rise in the development and application of artificial intelligence (AI) tools for use in pathology services has occurred. This trend is often expected to continue and reshape the field of pathology in the coming years. The deployment of computational pathology and applications of AI tools can be considered as a paradigm shift that will change pathology services, making them more efficient and capable of meeting the needs of this era of precision medicine. Despite the success of AI models, the translational process from discovery to clinical applications has been slow. The gap between self-contained research and clinical environment may be too wide and has been largely neglected. In this review, we cover the current and prospective applications of AI in pathology. We examine its applications in diagnosis and prognosis, and we offer insights for considerations that could improve clinical applicability of these tools. Then, we discuss its potential to improve workflow efficiency, and its benefits in pathologist education. Finally, we review the factors that could influence adoption in clinical practices and the associated regulatory processes.

  • telepathology
  • breast
  • pathology department, hospital
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Footnotes

  • Handling editor Runjan Chetty.

  • Correction notice This article has been corrected since it was published Online First. Author names and affiliations have been corrected.

  • Contributors All authors reviewed the paper.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Commissioned; internally peer-reviewed.

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