Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review.
BACKGROUND: Modern artificial intelligence (AI) and machine learning (ML) methods are now capable of completing tasks with performance characteristics that are comparable to those of expert human operators. As a result, many areas throughout healthcare are incorporating these technologies, including...
| Publicado en: | Clinical Chemistry Vol. 67; no. 11; pp. 1466 - 1483 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Oxford University Press / USA
Nov2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153642913&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153642913 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00099147 10CS jtl: Clinical Chemistry issn: 00099147 maglogo: N pubinfo: dt: Nov2021 vid: 67 iid: 11 pid: 622 pub: Oxford University Press / USA artinfo: ui: 153642913 10.1093/clinchem/hvab165 153642913 ppf: 1466 ppct: 17 formats: fmt: @attributes: type: P tig: atl: Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review. aug: au: Herman, Daniel S. Rhoads, Daniel D. Schulz, Wade L. Durant, Thomas J. S. affil: Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA sug: ab: BACKGROUND: Modern artificial intelligence (AI) and machine learning (ML) methods are now capable of completing tasks with performance characteristics that are comparable to those of expert human operators. As a result, many areas throughout healthcare are incorporating these technologies, including in vitro diagnostics and, more broadly, laboratory medicine. However, there are limited literature reviews of the landscape, likely future, and challenges of the application of AI/ML in laboratory medicine. CONTENT: In this review, we begin with a brief introduction to AI and its subfield of ML. The ensuing sections describe ML systems that are currently in clinical laboratory practice or are being proposed for such use in recent literature, ML systems that use laboratory data outside the clinical laboratory, challenges to the adoption of ML, and future opportunities for ML in laboratory medicine. SUMMARY: AI and ML have and will continue to influence the practice and scope of laboratory medicine dramatically. This has been made possible by advancements in modern computing and the widespread digitization of health information. These technologies are being rapidly developed and described, but in comparison, their implementation thus far has been modest. To spur the implementation of reliable and sophisticated ML-based technologies, we need to establish best practices further and improve our information system and communication infrastructure. The participation of the clinical laboratory community is essential to ensure that laboratory data are sufficiently available and incorporated conscientiously into robust, safe, and clinically effective MLsupported clinical diagnostics. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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