Bridging the Clinical-Computational Transparency Gap in Digital Pathology.
Context.--Computational pathology combines clinical pathology with computational analysis, aiming to enhance diagnostic capabilities and improve clinical productivity. However, communication barriers between pathologists and developers often hinder the full realization of this potential. Objective.-...
| Publicado en: | Archives of Pathology & Laboratory Medicine Vol. 149; no. 3; pp. 276 - 288 |
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| Autores principales: | , , , , , , , |
| Formato: | pictorial review tables/charts Journal Article |
| Publicado: |
College of American Pathologists
Mar2025
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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=183307757&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183307757 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00039985 1FS jtl: Archives of Pathology & Laboratory Medicine issn: 00039985 maglogo: N pubinfo: dt: Mar2025 vid: 149 iid: 3 pid: 2550 pub: College of American Pathologists place: Northfield, Illinois artinfo: ui: 183307757 183307757 183307757 10.5858/arpa.2023-0250-RA 183307757 ppf: 276 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Bridging the Clinical-Computational Transparency Gap in Digital Pathology. aug: au: Qiangqiang Gu Patel, Ankush Hanna, Matthew G. Lennerz, Jochen K. Garcia, Chris Zarella, Mark McClintock, David Hart, Steven N. affil: Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota sug: subj: Digital Imaging Image Processing, Computer Assisted Pathology, Clinical Diagnosis, Computer Assisted Pathologists Algorithms Pathology Professional Practice Machine Learning Microscopy, Virtual Patient Care ab: Context.--Computational pathology combines clinical pathology with computational analysis, aiming to enhance diagnostic capabilities and improve clinical productivity. However, communication barriers between pathologists and developers often hinder the full realization of this potential. Objective.--To propose a standardized framework that improves mutual understanding of clinical objectives and computational methodologies. The goal is to enhance the development and application of computer-aided diagnostic (CAD) tools. Design.--This article suggests pivotal roles for pathologists and computer scientists in the CAD development process. It calls for increased understanding of computational terminologies, processes, and limitations among pathologists. Similarly, it argues that computer scientists should better comprehend the true use cases of the developed algorithms to avoid clinically meaningless metrics. Results.--CAD tools improve pathology practice significantly. Some tools have even received US Food and Drug Administration approval. However, improved understanding of machine learning models among pathologists is essential to prevent misuse and misinterpretation. There is also a need for a more accurate representation of the algorithms' performance compared to that of pathologists. Conclusions.--A comprehensive understanding of computational and clinical paradigms is crucial for overcoming the translational gap in computational pathology. This mutual comprehension will improve patient care through more accurate and efficient disease diagnosis. pubtype: Academic Journal doctype: pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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