دورية أكاديمية
Challenges in the Development, Deployment, and Regulation of Artificial Intelligence in Anatomic Pathology.
| العنوان: | Challenges in the Development, Deployment, and Regulation of Artificial Intelligence in Anatomic Pathology. |
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| المؤلفون: | Cheng JY; Department of Pathology, University of Michigan, Ann Arbor, Michigan. Electronic address: jeromech@med.umich.edu., Abel JT; Department of Pathology, University of Michigan, Ann Arbor, Michigan., Balis UGJ; Department of Pathology, University of Michigan, Ann Arbor, Michigan., McClintock DS; Department of Pathology, University of Michigan, Ann Arbor, Michigan., Pantanowitz L; Department of Pathology, University of Michigan, Ann Arbor, Michigan. |
| المصدر: | The American journal of pathology [Am J Pathol] 2021 Oct; Vol. 191 (10), pp. 1684-1692. Date of Electronic Publication: 2020 Nov 24. |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't; Review |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Country of Publication: United States NLM ID: 0370502 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1525-2191 (Electronic) Linking ISSN: 00029440 NLM ISO Abbreviation: Am J Pathol Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: 2011-: New York : Elsevier Original Publication: Philadelphia [etc.] American Assn. of Pathologists [etc.] |
| مواضيع طبية MeSH: | Artificial Intelligence* , Pathology*, Cloud Computing ; Humans ; Pathologists ; Practice Patterns, Physicians' ; Social Control, Formal |
| مستخلص: | Significant advances in artificial intelligence (AI), deep learning, and other machine-learning approaches have been made in recent years, with applications found in almost every industry, including health care. AI is capable of completing a spectrum of mundane to complex medically oriented tasks previously performed only by boarded physicians, most recently assisting with the detection of cancers difficult to find on histopathology slides. Although computers will likely not replace pathologists any time soon, properly designed AI-based tools hold great potential for increasing workflow efficiency and diagnostic accuracy in pathology. Recent trends, such as data augmentation, crowdsourcing for generating annotated data sets, and unsupervised learning with molecular and/or clinical outcomes versus human diagnoses as a source of ground truth, are eliminating the direct role of pathologists in algorithm development. Proper integration of AI-based systems into anatomic-pathology practice will necessarily require fully digital imaging platforms, an overhaul of legacy information-technology infrastructures, modification of laboratory/pathologist workflows, appropriate reimbursement/cost-offsetting models, and ultimately, the active participation of pathologists to encourage buy-in and oversight. Regulations tailored to the nature and limitations of AI are currently in development and, when instituted, are expected to promote safe and effective use. This review addresses the challenges in AI development, deployment, and regulation to be overcome prior to its widespread adoption in anatomic pathology. (Copyright © 2020 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.) |
| تواريخ الأحداث: | Date Created: 20201127 Date Completed: 20211019 Latest Revision: 20211019 |
| رمز التحديث: | 20231215 |
| DOI: | 10.1016/j.ajpath.2020.10.018 |
| PMID: | 33245914 |
| قاعدة البيانات: | MEDLINE |
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Full Text Finder | تدمد: | 1525-2191 |
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| DOI: | 10.1016/j.ajpath.2020.10.018 |