Entering a new era of laboratory data processing and interpretation.
The article discusses the evolving landscape of laboratory medicine, focusing on the integration of refined statistical methods and artificial intelligence (AI) systems to enhance data processing and interpretation. It highlights seven articles that explore sustainable reference intervals (RIs) and...
| Publicado en: | Journal of Laboratory Medicine Vol. 49; no. 6; pp. 235 - 238 |
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| Autores principales: | , |
| Formato: | editorial tables/charts Journal Article |
| Publicado: |
De Gruyter
Dec2025
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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=189736964&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189736964 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 25679430 N1AK jtl: Journal of Laboratory Medicine issn: 25679430 maglogo: N pubinfo: dt: Dec2025 vid: 49 iid: 6 pid: 1734 pub: De Gruyter place: , <Blank> artinfo: ui: 189736964 189406526 189736964 189736964 10.1515/labmed-2025-0260 189736964 ppf: 235 ppct: 3 formats: tig: atl: Entering a new era of laboratory data processing and interpretation. aug: au: Hoffmann, Georg Klawonn, Frank affil: Trillium GmbH, Jesenwanger Straße 42b 82284, Grafrath, Germany sug: subj: Data Management Clinical Laboratories Informatics Laboratories Access to Information Artificial Intelligence Utilization Diagnosis, Laboratory Reference Values Decision Making, Clinical Quality Assurance ab: The article discusses the evolving landscape of laboratory medicine, focusing on the integration of refined statistical methods and artificial intelligence (AI) systems to enhance data processing and interpretation. It highlights seven articles that explore sustainable reference intervals (RIs) and clinical decision limits (CDLs), emphasizing the importance of understanding these concepts for effective clinical action. The discussion includes advancements in statistical tools, the role of machine learning in reference interval estimation, and the potential of large language models (LLMs) in therapeutic drug monitoring reporting. Additionally, it addresses the need for AI literacy among laboratory professionals, particularly in light of the EU AI Act, which mandates training in AI systems. Overall, the article underscores the dual responsibility of utilizing advanced tools while maintaining a deep understanding of their implications in clinical practice. pubtype: Academic Journal doctype: editorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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