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Review ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Med. | doi: 10.3389/fmed.2019.00185

Translational AI and Deep learning in Diagnostic Pathology

 Ahmed Serag1, Adrian Ion-Margineanu1, Hammad Qureshi1, Ryan J. McMillan1, Marie-Judith Saint Martin1, James Diamond1, Paul O'Reilly1 and  Peter W. Hamilton2*
  • 1Life Sciences R&D Hub, Philips Pathology Solutions, United Kingdom
  • 2Philips (Netherlands), Netherlands

There has been an exponential growth in the application of AI in health and in pathology. This is resulting in the innovation of deep learning technologies that are specifically aimed at cellular imaging and practical applications that could transform diagnostic pathology. This paper reviews the different approaches to deep learning in pathology, the public grand challenges that have driven this innovation and a range of emerging applications in pathology. The translation of AI into clinical practice will require applications to be embedded seamlessly within digital pathology workflows, driving an integrated approach to diagnostics and providing pathologists with new tools that accelerate workflow and improve diagnostic consistency and reduce errors. The clearance of digital pathology for primary diagnosis in the US by some manufacturers provides the platform on which to deliver practical AI. AI and computational pathology will continue to mature as researchers, clinicians, industry, regulatory organizations and patient advocacy groups work together to innovate and deliver new technologies to health care providers: technologies which are better, faster, cheaper, more precise and safe.

Keywords: Pathology,, digital pathology, artificial intelligence, machine learning, deep learning, Computational Pathology, precision medicine, neural networks, image analysis

Received: 02 Apr 2019; Accepted: 30 Jul 2019.

Copyright: © 2019 Serag, Ion-Margineanu, Qureshi, McMillan, Saint Martin, Diamond, O'Reilly and Hamilton. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Peter W. Hamilton, Philips (Netherlands), Amsterdam, Netherlands,