[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117235-en":3,"doc-seo-117235-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117235,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",7,"Healthcare","Machine learning in practice - Evaluation of Clinical Value, Guidelines","Machine learning research in health care has expanded rapidly, creating a gap between early publications demonstrating implementations and the slower development of orienting guidelines and recommendation statements. This imbalance obstructs consistent evaluation of the clinical value of machine learning studies and applications. The chapter emphasizes evaluation as a continuous process that supports performance assessment, repeatability, optimization, and reduction of research waste. It also outlines the need for machine learning frameworks to guide reporting and evaluating clinical value, and reviews emerging recommendations and guidelines.","University of Groningen  \nMachine learning in practice-Evaluation of clinical value, guidelines  \nJuarez-Orozco, Luis Eduardo; Ruijsink, Bram; Yeung, Ming Wai; Benjamins, Jan Walter; van der Harst, Pim  \nPublished in:  \nClinical Applications of Artificial Intelligence in Real-World Data  \nDOI:  \n10. 1007/978-3-031-36678-9_ 16  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2023  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nJuarez-Orozco, L. E. , Ruijsink, B. , Yeung, M. W. , Benjamins, J. W. , & van der Harst, P. (2023) . Machine learning in practice-Evaluation of clinical value, guidelines. In F. W. Asselbergs, S. Denaxas, D. L. Oberski,& J. H. Moore (Eds.), Clinical Applications of Artificial Intelligence in Real-World Data (pp. 247-261) .  \nSpringer International Publishing AG. 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More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 29-12-2025  \nMachine Learning in Practice—Evaluation of Clinical Value, Guidelines  \nLuis Eduardo Juarez-Orozco, Bram Ruijsink, Ming Wai Yeung, Jan Walter Benjaminsand Pim van der Harst  \nAbstract  \nMachine learning research in health care literature has grown at an unprecedented pace. This development has generated a clear disparity between the number of first publications involving machine learning implementations and that of orienting guidelines and recommendation statements to promote quality and report standardization. In turn, this hinders the much-needed evaluation of the clinical value of machine learning studies and applications. This appraisal should constitute a continuous process that allows performance evaluation, facilitates repeatability, leads optimization and boost clinical  \nL. E. Juarez-Orozco · B. Ruijsink · M. W. Yeung ·  \nP. van der Harst (*)  \nDepartment of Cardiology, Heart and Lungs Division, University Medical Center Utrecht, Utrecht, The Netherlands [e-mail: P.vanderHarst@umcutrecht.nl](e-mail: P.vanderHarst@umcutrecht.nl)  \nL. E. Juarez-Orozco  \nTurku PET Centre, University of Turku, 20520 Turku, Kiinamyllynkatu 4-8, Finland  \nB. Ruijsink  \nImaging Sciences and Biomedical Engineering, King’s College London, St Thomas’Hospital, London WC2R 2LS, United Kingdom  \nM. W. Yeung · J. W. Benjamins ·  \nP. van der Harst  \nDepartment of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands  \nvalue while minimizing research waste. The present chapter outlines the need for machine learning frameworks in healthca","cbCaicQSciXgaWaR","https://ap.wps.com/l/cbCaicQSciXgaWaR","pdf",604783,1,16,"English","en",105,"# Introduction\n## Need for clinical evaluation and guidelines\n## Disparity between implementations and recommendations\n## Continuous evaluation and framework requirements","[{\"question\":\"Why does the chapter highlight a disparity between machine learning implementations and guidelines?\",\"answer\":\"The growth of first-line machine learning publications has outpaced the creation of guidelines and recommendations for quality and standardized reporting, making evaluation harder.\"},{\"question\":\"What does the chapter define as essential for evaluating clinical value?\",\"answer\":\"Evaluation should be continuous, enabling performance assessment, repeatability, and optimization while minimizing research waste.\"},{\"question\":\"What role do machine learning frameworks play in healthcare research?\",\"answer\":\"Frameworks are needed to guide efforts in reporting and evaluating clinical value, bridging the gap between data science and clinical medicine and supporting emerging recommendations.\"}]","Machine learning in practice - 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