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Artificial Intelligence in the Clinical Laboratory

An Overview with Frequently Asked Questions
Published:December 13, 2022DOI:https://doi.org/10.1016/j.cll.2022.09.002

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      References

        • Baron J.M.
        • Dighe A.S.
        Computerized provider order entry in the clinical laboratory.
        J Pathol Inform. 2011; 2 ([published Online First: Epub Date]): 35
        • Baron J.M.
        • Dighe A.S.
        • Arnaout R.
        • et al.
        The 2013 symposium on pathology data integration and clinical decision support and the current state of field.
        J Pathol Inform. 2014; 5 ([published Online First: Epub Date]): 2
        • Baron J.M.
        • Dighe A.S.
        The role of informatics and decision support in utilization management.
        Clin Chim Acta. 2014; 427 ([published Online First: Epub Date]): 196-201
        • Baron J.M.
        • Kurant D.E.
        • Dighe A.S.
        Machine learning and other emerging decision support tools.
        Clin Lab Med. 2019; 39 ([published Online First: Epub Date]|): 319-331
        • Louis D.N.
        • Feldman M.
        • Carter A.B.
        • et al.
        Computational pathology.
        Arch Pathol Lab Med. 2015; ([published Online First: Epub Date]|)https://doi.org/10.5858/arpa.2015-0093-SA
        • Louis D.N.
        • Gerber G.K.
        • Baron J.M.
        • et al.
        Computational pathology: an emerging definition.
        Arch Pathol Lab Med. 2014; 138 ([published Online First: Epub Date]|): 1133-1138
        • Lee L.H.
        • Mansoor A.
        • Wood B.
        • et al.
        Performance of CellaVision DM96 in leukocyte classification.
        J Pathol Inform. 2013; 4 ([published Online First: Epub Date]|): 14
        • Baron J.M.
        • Paranjape K.
        • Love T.
        • et al.
        Development of a "meta-model" to address missing data, predict patient-specific cancer survival and provide a foundation for clinical decision support.
        J Am Med Inform Assoc. 2021; 28 ([published Online First: Epub Date]|): 605-615
        • Ibrahim A.
        • Gamble P.
        • Jaroensri R.
        • et al.
        Artificial intelligence in digital breast pathology: techniques and applications.
        Breast. 2020; 49 ([published Online First: Epub Date]|): 267-273
        • Jahn S.W.
        • Plass M.
        • Moinfar F.
        Digital pathology: advantages, limitations and emerging perspectives.
        J Clin Med. 2020; 9 ([published Online First: Epub Date]|)
        • Lin E.
        • Lin C.H.
        • Lane H.Y.
        Machine learning and deep learning for the pharmacogenomics of antidepressant treatments.
        Clin Psychopharmacol Neurosci. 2021; 19 ([published Online First: Epub Date]|): 577-588
        • Dimitriou N.
        • Arandjelovic O.
        • Caie P.D.
        Deep learning for whole slide image analysis: an overview.
        Front Med (Lausanne). 2019; 6 ([published Online First: Epub Date]|): 264
        • Fleuren L.M.
        • Klausch T.L.T.
        • Zwager C.L.
        • et al.
        Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy.
        Intensive Care Med. 2020; 46 ([published Online First: Epub Date]|): 383-400
        • Krittanawong C.
        • Zhang H.
        • Wang Z.
        • et al.
        Artificial intelligence in precision cardiovascular medicine.
        J Am Coll Cardiol. 2017; 69 ([published Online First: Epub Date]|): 2657-2664
        • Rosenbaum M.W.
        • Baron J.M.
        Using machine learning-based multianalyte delta checks to detect wrong blood in tube errors.
        Am J Clin Pathol. 2018; 150 ([published Online First: Epub Date]|): 555-566
        • Luo Y.
        • Szolovits P.
        • Dighe A.S.
        • et al.
        Using machine learning to predict laboratory test results.
        Am J Clin Pathol. 2016; 145 ([published Online First: Epub Date]|): 778-788
        • Baron J.M.
        • Huang R.
        • McEvoy D.
        • et al.
        Use of machine learning to predict clinical decision support compliance, reduce alert burden, and evaluate duplicate laboratory test ordering alerts.
        JAMIA Open. 2021; 4 ([published Online First: Epub Date]|): ooab006
        • Zeng Z.
        • Deng Y.
        • Li X.
        • et al.
        natural language processing for EHR-based computational phenotyping.
        Ieee/acm Trans Comput Biol Bioinform. 2019; 16 ([published Online First: Epub Date]|): 139-153
        • Chou R.
        • Wasson N.
        Blood tests to diagnose fibrosis or cirrhosis in patients with chronic hepatitis C virus infection: a systematic review.
        Ann Intern Med. 2013; 158 ([published Online First: Epub Date]|): 807-820
        • Baron J.M.
        • Mermel C.H.
        • Lewandrowski K.B.
        • et al.
        Detection of preanalytic laboratory testing errors using a statistically guided protocol.
        Am J Clin Pathol. 2012; 138 ([published Online First: Epub Date]): 406-413
      1. R Core Team. R: A Language and Environment for Statistical Computing, 2013. Available at: https://cran.microsoft.com/snapshot/2014-09-08/web/packages/dplR/vignettes/xdate-dplR.pdf.

        • Pedregosa F.
        • Varoquaux G.
        • Gramfort A.
        • et al.
        Scikit-learn: machine learning in Python.
        J machine Learn Res. 2011; 12: 2825-2830
        • Bates D.W.
        • Auerbach A.
        • Schulam P.
        • et al.
        Reporting and implementing interventions involving machine learning and artificial intelligence.
        Ann Intern Med. 2020; 172 ([published Online First: Epub Date]|): S137-S144
        • McBee M.P.
        • Awan O.A.
        • Colucci A.T.
        • et al.
        Deep learning in radiology.
        Acad Radiol. 2018; 25 ([published Online First: Epub Date]|): 1472-1480
        • Esteva A.
        • Robicquet A.
        • Ramsundar B.
        • et al.
        A guide to deep learning in healthcare.
        Nat Med. 2019; 25 (published Online First: Epub Date]|): 24-29
        • Miotto R.
        • Wang F.
        • Wang S.
        • et al.
        Deep learning for healthcare: review, opportunities and challenges.
        Brief Bioinform. 2018; 19 ([published Online First: Epub Date]|): 1236-1246
        • Boussadi A.
        • Zapletal E.
        A fast healthcare interoperability resources (FHIR) layer implemented over i2b2.
        BMC Med Inform Decis Mak. 2017; 17 ([published Online First: Epub Date]|): 120
        • Mandel J.C.
        • Kreda D.A.
        • Mandl K.D.
        • et al.
        SMART on FHIR: a standards-based, interoperable apps platform for electronic health records.
        J Am Med Inform Assoc. 2016; 23 ([published Online First: Epub Date]|): 899-908