Blog
About

0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Physician perspectives on integration of artificial intelligence into diagnostic pathology

      Read this article at

      Bookmark
          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

          Advancements in computer vision and artificial intelligence (AI) carry the potential to make significant contributions to health care, particularly in diagnostic specialties such as radiology and pathology. The impact of these technologies on physician stakeholders is the subject of significant speculation. There is however a dearth of information regarding the opinions, enthusiasm, and concerns of the pathology community at large. Here, we report results from a survey of 487 pathologist-respondents practicing in 54 countries, conducted to examine perspectives on AI implementation in clinical practice. Despite limitations, including difficulty with quantifying response bias and verifying identity of respondents to this anonymous and voluntary survey, several interesting findings were uncovered. Overall, respondents carried generally positive attitudes towards AI, with nearly 75% reporting interest or excitement in AI as a diagnostic tool to facilitate improvements in workflow efficiency and quality assurance in pathology. Importantly, even within the more optimistic cohort, a significant number of respondents endorsed concerns about AI, including the potential for job displacement and replacement. Overall, around 80% of respondents predicted the introduction of AI technology in the pathology laboratory within the coming decade. Attempts to identify statistically significant demographic characteristics (e.g., age, sex, type/place of practice) predictive of attitudes towards AI using Kolmogorov–Smirnov (KS) testing revealed several associations. Important themes which were commented on by respondents included the need for increasing efforts towards physician training and resolving medical-legal implications prior to the generalized implementation of AI in pathology.

          Related collections

          Most cited references 18

          • Record: found
          • Abstract: not found
          • Article: not found

          Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

            Pathologists face a substantial increase in workload and complexity of histopathologic cancer diagnosis due to the advent of personalized medicine. Therefore, diagnostic protocols have to focus equally on efficiency and accuracy. In this paper we introduce ‘deep learning’ as a technique to improve the objectivity and efficiency of histopathologic slide analysis. Through two examples, prostate cancer identification in biopsy specimens and breast cancer metastasis detection in sentinel lymph nodes, we show the potential of this new methodology to reduce the workload for pathologists, while at the same time increasing objectivity of diagnoses. We found that all slides containing prostate cancer and micro- and macro-metastases of breast cancer could be identified automatically while 30–40% of the slides containing benign and normal tissue could be excluded without the use of any additional immunohistochemical markers or human intervention. We conclude that ‘deep learning’ holds great promise to improve the efficacy of prostate cancer diagnosis and breast cancer staging.
              Bookmark
              • Record: found
              • Abstract: not found
              • Book Chapter: not found

              Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks

                Bookmark

                Author and article information

                Contributors
                randy.vanommeren@mail.utoronto.ca
                p.diamandis@mail.utoronto.ca
                Journal
                NPJ Digit Med
                NPJ Digit Med
                NPJ Digital Medicine
                Nature Publishing Group UK (London )
                2398-6352
                26 April 2019
                26 April 2019
                2019
                : 2
                Affiliations
                [1 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, Department of Laboratory Medicine and Pathobiology, , University of Toronto, ; Toronto, Ontario M5S 1A8 Canada
                [2 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, Department of Computer Science, , University of Toronto, ; 40 St. George Street, Toronto, Ontario M5S 2E4 Canada
                [3 ]Princess Margaret Cancer Centre, MacFeeters Hamilton Centre for Neuro-Oncology Research, 101 College Street, Toronto, Ontario M5G 1L7 Canada
                [4 ]ISNI 0000 0004 0474 0428, GRID grid.231844.8, Laboratory Medicine Program, Department of Pathology, , University Health Network, ; 200 Elizabeth Street, Toronto, ON M5G 2C4 Canada
                Article
                106
                10.1038/s41746-019-0106-0
                6550202
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                Funding
                Funded by: American Society of Clinical Oncology Conquer Cancer Foundation The Brain Tumour Foundation of Canada
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                health care, health occupations

                Comments

                Comment on this article