52
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Digital pathology and artificial intelligence

      , ,
      The Lancet Oncology
      Elsevier BV

      Read this article at

      ScienceOpenPublisherPMC
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          <p class="first" id="d4376923e89">In modern clinical practice, digital pathology has a crucial role and is increasingly a technological requirement in the scientific laboratory environment. The advent of whole-slide imaging, availability of faster networks, and cheaper storage solutions has made it easier for pathologists to manage digital slide images and share them for clinical use. In parallel, unprecedented advances in machine learning have enabled the synergy of artificial intelligence and digital pathology, which offers image-based diagnosis possibilities that were once limited only to radiology and cardiology. Integration of digital slides into the pathology workflow, advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enable true utilisation and integration of knowledge that is beyond human limits and boundaries, and we believe there is clear potential for artificial intelligence breakthroughs in the pathology setting. In this Review, we discuss advancements in digital slide-based image diagnosis for cancer along with some challenges and opportunities for artificial intelligence in digital pathology. </p>

          Related collections

          Author and article information

          Journal
          The Lancet Oncology
          The Lancet Oncology
          Elsevier BV
          14702045
          May 2019
          May 2019
          : 20
          : 5
          : e253-e261
          Article
          10.1016/S1470-2045(19)30154-8
          8711251
          31044723
          9db27f99-677d-4a8d-ad3b-8734bef400dd
          © 2019

          https://www.elsevier.com/tdm/userlicense/1.0/

          History

          Comments

          Comment on this article

          Related Documents Log