[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119560-en":3,"doc-seo-119560-105":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},119560,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning in Primary Health Care - The Research Landscape","Artificial intelligence and machine learning drive digital transformation for primary health systems by improving efficiency, effectiveness, equity, and responsiveness. The study applies synthetic knowledge synthesis combined with bibliometric and thematic analysis triangulation to map the research landscape. Results highlight the most productive countries (United States, United Kingdom), journals (PLOS ONE, BJM Open), and funding sponsors (National Institutes of Health, USA; National Natural Science Foundation of China). Publication trends increase exponentially. Key themes include clinical decision support via natural language processing, primary care optimization for early diagnosis and screening, advancing social determinants, and patient and clinician communication through chatbots. The work targets missed diagnostic opportunities while reducing adverse effects.","Review  \nMachine Learning in Primary Health Care: The Research Landscape  \nJernej Završnik 1,2, Peter Kokol 1,3, *, Bojan Žlahtiˇc 3 and Helena Blažun Vošner 1,2  \nAcademic Editor: Thomas Yuen TungLam  \nReceived: 15 May 2025  \nRevised: 14 June 2025  \nAccepted: 23 June 2025  \nPublished: 7 July 2025  \nCitation: Završnik, J.; Kokol, P.;Žlahtiˇc, B.; Blažun Vošner, H. Machine Learning in Primary HealthCare: The Research Landscape. Healthcare 2025, 13, 1629. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)healthcare13131629  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Community Healthcare Center Dr. Adolf Drolc Maribor, 2000 Maribor, Slovenia; [jernej.zavrsnik@zd-mb.si](jernej.zavrsnik@zd-mb.si) (J.Z.); [helena.blazun@zd-mb.si](helena.blazun@zd-mb.si) (H.B.V.)  \n2 Alma Mater Europaea, 2000 Maribor, Slovenia  \n3 Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia  \n* [Correspondence: peter.kokol@um.si](Correspondence: peter.kokol@um.si)  \nAbstract  \nBackground: Artificial intelligence and machine learning are playing crucial roles in digital transformation, aiming to improve the efficiency, effectiveness, equity, and responsiveness of primary health systems and their services. Method: Using synthetic knowledge synthesis and bibliometric and thematic analysis triangulation, we identified the most productive and prolific countries, institutions, funding sponsors, source titles, publications productivity trends, and principal research categories and themes. Results: The United States and the United Kingdom were the most productive countries; Plos One and BJM Open were the most prolific journals; and the National Institutes of Health, USA, and the National Natural Science Foundation of China were the most productive funding sponsors. The publication productivity trend is positive and exponential. The main themes are related to natural language processing in clinical decision-making, primary health care optimization focusing on early diagnosis and screening, improving health-based social determinants, and using chatbots to optimize communications with patients and between health professionals. Conclusions: The use of machine learning in primary health care aims to address the significant global burden of so-called “missed diagnostic opportunities” while minimizing possible adverse effects on patients.  \nKeywords: primary health care; machine learning; research landscape; synthetic knowledge synthesis  \n1. Introduction  \nThe development of digital health can empower equitable access to global expert-level health care and transform health care into a more value-based, equitable, and patient-centric system [1,2] . Artificial intelligence is essential to this transformation at both the general [3] and primary health care levels [4,5] . The use of machine learning, an essential part of artificial intelligence, is already showing promising results in primary health care [6–12] . In general, the use of machine learning in health care can improve efficiency by improving the following factors: health care service delivery [13,14], screening [15,16], health care cost management [17], equity by predicting missing appointments [13,18] or improving access to primary health care [19,20], responsiveness through better decision-making [21,22], and the monitoring of primary health services [23] . Recently, several reviews on machine learning use in primary health care have been published. However, these reviews were not oriented toward primary health as a whole but were limited to specific diseases,  \nconditions, prognostic or prediction modeling, or specific health care services ","cbCaikUc3t89Fh0L","https://ap.wps.com/l/cbCaikUc3t89Fh0L","pdf",908476,1,15,"English","en",105,"# Abstract\n# Introduction\n# Materials and Methods","[{\"question\":\"What is the main purpose of using machine learning in primary health care in this study?\",\"answer\":\"It aims to address missed diagnostic opportunities and reduce potential adverse effects on patients while improving primary health services.\"},{\"question\":\"Which research methods are used to build the machine learning research landscape?\",\"answer\":\"Synthetic knowledge synthesis is used together with bibliometric and thematic analysis triangulation, including bibliometric mapping and content analysis.\"},{\"question\":\"What major themes emerge from the mapped literature?\",\"answer\":\"Themes include natural language processing for clinical decision-making, early diagnosis and screening optimization, improving social determinants, and chatbot-based communication with patients and health professionals.\"}]","Machine Learning in Primary Health Care - 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