Translational AI and Deep Learning in Diagnostic Pathology

Ahmed Serag, Adrian Ion-Margineanu, Hammad Qureshi, Ryan McMillan, Marie-Judith Saint Martin, Jim Diamond, Paul O'Reilly, Peter Hamilton
<span title="2019-10-01">2019</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="" style="color: black;">Frontiers in Medicine</a> </i> &nbsp;
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
more &raquo; ... ctice 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.3389/fmed.2019.00185</a> <a target="_blank" rel="external noopener" href="">pmid:31632973</a> <a target="_blank" rel="external noopener" href="">pmcid:PMC6779702</a> <a target="_blank" rel="external noopener" href="">fatcat:jd5arv2lc5aq3lbk7ztipt5mca</a> </span>
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