Introduction to Artificial Intelligence and Machine Learning for Pathology.
| Autor: | Harrison JH; From the Department of Pathology, University of Virginia School of Medicine, Charlottesville (Harrison)., Gilbertson JR; The Departments of Biomedical Informatics and Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania (Gilbertson)., Hanna MG; The Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York (Hanna)., Olson NH; The Defense Innovation Unit, Mountain View, California (Olson).; The Department of Pathology, Uniformed Services University, Bethesda, Maryland (Olson)., Seheult JN; The Department of Pathology, University of Pittsburgh, and Vitalant Specialty Labs, Pittsburgh, Pennsylvania (Seheult)., Sorace JM; The US Department of Health and Human Services, retired, Lutherville, Maryland (Sorace)., Stram MN; The Department of Forensic Medicine, New York University, and Office of Chief Medical Examiner, New York, New York (Stram). |
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| Jazyk: | angličtina |
| Zdroj: | Archives of pathology & laboratory medicine [Arch Pathol Lab Med] 2021 Oct 01; Vol. 145 (10), pp. 1228-1254. |
| DOI: | 10.5858/arpa.2020-0541-CP |
| Abstrakt: | 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. Competing Interests: All authors are members of the Machine Learning Workgroup, College of American Pathologists Informatics Committee. |
| Databáze: | MEDLINE |
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