[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127350-en":3,"doc-seo-127350-105":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},127350,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Availability of Evidence for Predictive Machine Learning Algorithms in Primary Care - A Systematic Review","Predictive machine learning (ML) algorithms have potential to support primary health care amid aging populations, multimorbidity, and workforce shortages, yet public concerns remain about transparency and inadequate reporting of validation, effectiveness, and implementation in clinical workflows. This systematic review identifies predictive ML algorithms in primary care from peer-reviewed literature and FDA/CE registration databases, and evaluates public evidence availability across the AI life cycle. Searches covered studies from January 2000 to July 2023, yielding 43 algorithms; evidence availability varied and was limited overall.","Availability of evidence for predictive machine learning algorithms in primary care: a systematic review  \nRakers, M.M.; Buchem, M.M. van; Kucenko, S.; Hond, [A. de](A. de); Kant, I.; Smeden, M. van;   ; Os, H.J.A. van  \nCitation  \nRakers, M. M., Buchem, M. M. van, Kucenko, S., Hond, [A. de](A. de), Kant, I., Smeden, M. van,   Os, H. J. A. van. (2024). Availability of evidence for predictive machine learning algorithms in primary care: a systematic review. Jama Network Open, 7(9) .  \ndoi:10.1001/jamanetworkopen.2024.32990  \nVersion: Publisher's Version  \nLicense:  Creative Commons CC BY 4.0 license  \nDownloaded from:  [https://hdl.handle.net/1887/4266745](https://hdl.handle.net/1887/4266745)  \nNote: To cite this publication please use the final published version (if applicable) .  \nOriginal Investigation | Health Informatics  \nAvailability of Evidence for Predictive Machine Learning Algorithms in Primary Care  \nA Systematic Review  \nMargot M. Rakers, MD; Marieke M. van Buchem, MSc; Sergej Kucenko, BSc; Anne de Hond, PhD; Ilse Kant, PhD; Maarten van Smeden, PhD;  \nKarel G. M. Moons, PhD; Artuur M. Leeuwenberg, PhD; Niels Chavannes, MD, PhD; María Villalobos-Quesada, PhD; Hendrikus J. A. van Os, MD, PhD  \n\n| Abstract\u003Cbr>IMPORTANCE The aging and multimorbid population and health personnel shortages pose a substantial burden on primary health care. While predictive machine learning (ML) algorithms have the potential to address these challenges, concerns include transparency and insufficient reporting of model validation and effectiveness of the implementation in the clinical workflow.\u003Cbr>OBJECTIVES To systematically identify predictive ML algorithms implemented in primary care from peer-reviewed literature and US Food and Drug Administration (FDA) and Conformité Européene (CE) registration databases and to ascertain the public availability of evidence, including peerreviewed literature, gray literature, and technical reports across the artificial intelligence (AI)\u003Cbr>life cycle.\u003Cbr>EVIDENCE REVIEW PubMed, Embase, Web of Science, Cochrane Library, Emcare, Academic Search Premier, IEEE Xplore, ACM Digital Library, MathSciNet, [AAAI.org](AAAI.org) (Association for the Advancement of Artificial Intelligence), arXiv, Epistemonikos, PsycINFO, and Google Scholar were searched for studies published between January 2000 and July 2023, with search terms that were related to AI, primary care, and implementation. The search extended to CE-marked or FDA-approved predictive ML algorithms obtained from relevant registration databases. Three reviewers gathered subsequent evidence involving strategies such as product searches, exploration of references, manufacturer website visits, and direct inquiries to authors and product owners. The extent to which the evidence for each predictive ML algorithm aligned with the Dutch AI predictive algorithm (AIPA) guideline requirements was assessed per AI life cycle phase, producing evidence availability scores.\u003Cbr>\u003Cbr>FINDINGS The systematic search identified 43 predictive ML algorithms, of which 25 were commercially available and CE-marked or FDA-approved. The predictive ML algorithms spanned multiple clinical domains, but most (27 [63%]) focused on cardiovascular diseases and diabetes. Most (35 [81%]) were published within the past 5 years. The availability of evidence varied across different phases ofthe predictive ML algorithm life cycle, with evidence being reported the least for phase 1 (preparation) and phase 5 (impact assessment) (19% and 30%, respectively). Twelve (28%) predictive ML algorithms achieved approximately half of their maximum individual evidence availability score. Overall, predictive ML algorithms from peer-reviewed literature showed higher evidence availability compared with those from FDA-approved or CE-marked databases (45% vs 29%) .\u003Cbr>CONCLUSIONSAND RELEVANCE The findings indicate an urgent need to improve the availability of evidence regarding the predictive ML algorithms’ quality","cbCaimWskimb8dGH","https://ap.wps.com/l/cbCaimWskimb8dGH","pdf",1431049,1,18,"English","en",105,"# Key Points\n# Abstract\n## Importance, Objectives, Evidence Review\n## Findings, Conclusions and Relevance\n# Introduction","[{\"question\":\"What was the main purpose of the systematic review?\",\"answer\":\"To identify predictive ML algorithms implemented in primary care and determine how much evidence is publicly available to support their quality across the AI life cycle.\"},{\"question\":\"Which sources were used to find predictive ML algorithms?\",\"answer\":\"Peer-reviewed literature was searched alongside US Food and Drug Administration (FDA) and Conformité Européene (CE) registration databases, with additional evidence gathered through product search strategies and direct inquiries.\"},{\"question\":\"What did the review find about evidence availability overall?\",\"answer\":\"Evidence availability was limited across AI life cycle phases, with development reported more often than other phases, and comparatively less evidence reported for early preparation and later impact assessment.\"}]","Availability of Evidence for Predictive Machine Learning Algorithms in Primary Care - 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