Introduction to Artificial Intelligence and Machine Learning for Pathology.
* Context.--Recent developments in machine learning have stimulated intense interest in software that may augment or replace human experts. Machine learning may impact pathology practice by offering new capabilities in analysis, interpretation, and outcomes prediction using images and other data. Th...
| Published in: | Archives of Pathology & Laboratory Medicine Vol. 145; no. 10; pp. 1228 - 1255 |
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| Main Authors: | , , , , , , |
| Format: | pictorial review tables/charts Journal Article |
| Published: |
College of American Pathologists
Oct2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152716882&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152716882 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00039985 1FS jtl: Archives of Pathology & Laboratory Medicine issn: 00039985 maglogo: N pubinfo: dt: Oct2021 vid: 145 iid: 10 pid: 2550 pub: College of American Pathologists place: Northfield, Illinois artinfo: ui: 152716882 152716882 152716882 10.5858/arpa.2020-0541-CP 152716882 ppf: 1228 ppct: 27 formats: fmt: @attributes: type: P tig: atl: Introduction to Artificial Intelligence and Machine Learning for Pathology. aug: au: Harrison Jr., James H. Gilbertson, John R. Hanna, Matthew G. Olson, Niels H. Seheult, Jansen N. Sorace, James M. Stram, Michelle N. affil: Department of Pathology, University of Virginia School of Medicine, Charlottesville sug: subj: Medical Practice Machine Learning Pathologists Education Pathology, Clinical Algorithms Models, Statistical Productivity Evaluation Professional Role Technology ab: * Context.--Recent developments in machine learning have stimulated intense interest in software that may augment or replace human experts. Machine learning may impact pathology practice by offering new capabilities in analysis, interpretation, and outcomes prediction using images and other data. The principles of operation and management of machine learning systems are unfamiliar to pathologists, who anticipate a need for additional education to be effective as expert users and managers of the new tools. Objective.--To provide a background on machine learning for practicing pathologists, including an overview of algorithms, model development, and performance evaluation; to examine the current status of machine learning in pathology and consider possible roles and requirements for pathologists in local deployment and management of machine learning systems; and to highlight existing challenges and gaps in deployment methodology and regulation. Data Sources.--Sources include the biomedical and engineering literature, white papers from professional organizations, government reports, electronic resources, and authors' experience in machine learning. References were chosen when possible for accessibility to practicing pathologists without specialized training in mathematics, statistics, or software development. Conclusions.--Machine learning offers an array of techniques that in recent published results show substantial promise. Data suggest that human experts working with machine learning tools outperform humans or machines separately, but the optimal form for this combination in pathology has not been established. Significant questions related to the generalizability of machine learning systems, local site verification, and performance monitoring remain to be resolved before a consensus on best practices and a regulatory environment can be established. pubtype: Academic Journal doctype: pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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