{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T01:19:46Z","timestamp":1773451186842,"version":"3.50.1"},"reference-count":84,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T00:00:00Z","timestamp":1747958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec><jats:title>Aim\/objective<\/jats:title><jats:p>This review aims to provide a comprehensive analysis of the integration of machine learning (ML) (1) in nursing by exploring its implications on patient care, nursing practices, and healthcare delivery. It highlights current applications, challenges, ethical considerations, and the potential future developments of ML in nursing.<\/jats:p><\/jats:sec><jats:sec><jats:title>Background<\/jats:title><jats:p>With the advent of ML in healthcare, the nursing profession stands on the cusp of a transformative era. Despite the technological advancements, discussions on the utilization of ML in nursing, which are crucial for advancing the profession, are lacking. This review seeks to fill this gap by examining the balance between technological innovation and the human-centric nature of nursing.<\/jats:p><\/jats:sec><jats:sec><jats:title>Design<\/jats:title><jats:p>This narrative review employs a detailed search strategy across several databases, including PubMed, Embase, MEDLINE, Scopus, and Web of Science. It focuses on articles that were published from January 2019 to December 2023. Moreover, this review aims to illustrate the current use, challenges, and future potential of ML applications in nursing.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Inclusion criteria targeted articles that focus on ML application in nursing, challenges, ethical considerations, and future directions. Exclusion criteria omitted opinion pieces and nonrelevant studies. Articles were categorized into themes, such as patient care, nursing education, operational efficiency, ethical considerations, and future potential, thus facilitating a structured analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Findings demonstrate that ML has significantly enhanced patient monitoring, predictive analytics, and preventive care. For example, the COMPOSER deep learning model for early sepsis prediction was associated with a 1.9% absolute reduction (17% relative decrease) in in-hospital sepsis mortality and a 5.0% absolute increase (10% relative increase) in sepsis bundle compliance. In nursing education, ML has improved simulation-based training by facilitating adaptive learning experiences that support continual skill development. Furthermore, ML contributes to operational efficiency through automated staffing optimization and administrative task automation, thus reducing nurse workload and enhancing patient care outcomes. However, key challenges include ethical considerations, such as data privacy, algorithmic bias, and patient autonomy, which necessitate ongoing research and regulatory oversight.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>ML in nursing offers transformative potential across patient care, education, and operational efficiency, which is balanced by significant challenges and ethical considerations. Future directions include expanding clinical and community applications, integrating emerging technologies, and enhancing nursing education. Continuous research, ethical oversight, and interdisciplinary collaboration are essential for harnessing ML's full potential in nursing to ensure that its advancements improve patient outcomes and support nursing professionals without compromising core nursing values.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fdgth.2025.1514133","type":"journal-article","created":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T05:29:08Z","timestamp":1747978148000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Leveraging machine learning in nursing: innovations, challenges, and ethical insights"],"prefix":"10.3389","volume":"7","author":[{"given":"Sophie So Wan","family":"Yip","sequence":"first","affiliation":[]},{"given":"Sheng","family":"Ning","sequence":"additional","affiliation":[]},{"given":"Niki Yan Ki","family":"Wong","sequence":"additional","affiliation":[]},{"given":"Jeffrey","family":"Chan","sequence":"additional","affiliation":[]},{"given":"Kei Shing","family":"Ng","sequence":"additional","affiliation":[]},{"given":"Bernadette Oi Ting","family":"Kwok","sequence":"additional","affiliation":[]},{"given":"Robert L.","family":"Anders","sequence":"additional","affiliation":[]},{"given":"Simon Ching","family":"Lam","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,5,23]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e0001308","DOI":"10.1371\/journal.pgph.0001308","article-title":"Safety and immunogenicity of a reduced dose of the BNT162b2 mRNA COVID-19 vaccine (REDU-VAC): a single blind, randomized, non-inferiority trial","volume":"2","author":"Pannus","year":"2022","journal-title":"PLOS Glob Public Health"},{"key":"B2","doi-asserted-by":"publisher","first-page":"2951","DOI":"10.1111\/jocn.16478","article-title":"Artificial intelligence in nursing and midwifery: a systematic review","volume":"32","author":"O'Connor","year":"2023","journal-title":"J Clin Nurs"},{"key":"B3","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1097\/01.Naj.0000742064.59610.28","article-title":"CE: nursing orientation to data science and machine learning","volume":"121","author":"O'Brien","year":"2021","journal-title":"Am J Nurs"},{"key":"B4","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1097\/cin.0000000000001102","article-title":"Data-centric machine learning in nursing: a concept clarification","volume":"42","author":"Ball Dunlap","year":"2024","journal-title":"Comput Inform Nurs"},{"key":"B5","doi-asserted-by":"publisher","first-page":"5","DOI":"10.4069\/kjwhn.2020.03.11","article-title":"Artificial intelligence, machine learning, and deep learning in women\u2019s health nursing","volume":"26","author":"Jeong","year":"2020","journal-title":"Korean J Women Health Nurs"},{"key":"B6","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1097\/NNA.0000000000000855","article-title":"Artificial intelligence and nursing: the future is now","volume":"50","author":"Clancy","year":"2020","journal-title":"J Nurs Adm"},{"key":"B7","doi-asserted-by":"publisher","first-page":"3931","DOI":"10.18203\/2320-6012.ijrms20233066","article-title":"Artificial intelligence for smart patient care: transforming future of nursing practice","volume":"11","author":"Hote","year":"2023","journal-title":"Int J Res Med Sci"},{"key":"B8","doi-asserted-by":"publisher","first-page":"279","DOI":"10.3390\/jpm10040279","article-title":"Profiling patients by intensity of nursing care: an operative approach using machine learning","volume":"10","author":"Ocagli","year":"2020","journal-title":"J Pers Med"},{"key":"B9","doi-asserted-by":"publisher","first-page":"7456","DOI":"10.3390\/s23177456","article-title":"Spectrum evaluation in CR-based smart healthcare systems using optimizable tree machine learning approach","volume":"23","author":"Raza","year":"2023","journal-title":"Sensors (Basel)"},{"key":"B10","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s41073-019-0064-8","article-title":"SANRA\u2014a scale for the quality assessment of narrative review articles","volume":"4","author":"Baethge","year":"2019","journal-title":"Res Integr Peer Rev"},{"key":"B11","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1038\/s41746-023-00986-6","article-title":"Impact of a deep learning sepsis prediction model on quality of care and survival","volume":"7","author":"Boussina","year":"2024","journal-title":"npj Digit Med"},{"key":"B12","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1186\/s12874-021-01284-z","article-title":"Application of machine learning in predicting hospital readmissions: a scoping review of the literature","volume":"21","author":"Huang","year":"2021","journal-title":"BMC Med Res Methodol"},{"key":"B13","article-title":"Before and After: Machine Learning for Perioperative Patient Care","author":"Ganskaia","year":"2022"},{"key":"B14","doi-asserted-by":"publisher","first-page":"4188","DOI":"10.3390\/jcm12134188","article-title":"Revolutionizing spinal care: current applications and future directions of artificial intelligence and machine learning","volume":"12","author":"Yagi","year":"2023","journal-title":"J Clin Med"},{"key":"B15","doi-asserted-by":"publisher","first-page":"1091885","DOI":"10.3389\/fcvm.2022.1091885","article-title":"Development of an interpretable machine learning-based intelligent system of exercise prescription for cardio- oncology preventive care: a study protocol","volume":"9","author":"Gao","year":"2022","journal-title":"Front Cardiovasc Med"},{"key":"B16","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1186\/s12913-022-08615-w","article-title":"Early prediction of patient discharge disposition in acute neurological care using machine learning","volume":"22","author":"Mickle","year":"2022","journal-title":"BMC Health Serv Res"},{"key":"B17","doi-asserted-by":"publisher","first-page":"e45006","DOI":"10.7759\/cureus.45006","article-title":"Healthcare in Vietnam: harnessing artificial intelligence and robotics to improve patient care outcomes","volume":"15","author":"Doan Thu","year":"2023","journal-title":"Cureus"},{"key":"B18","doi-asserted-by":"publisher","first-page":"01","DOI":"10.54393\/pjhs.v4i05.844","article-title":"Transforming healthcare through artificial intelligence and machine learning","volume":"4","author":"Naveed","year":"2023","journal-title":"Pak J Health Sci"},{"key":"B19","article-title":"Artificial intelligence techniques for the wireless wearable smart healthcare prediction system applications","author":"Padma","year":""},{"key":"B20","doi-asserted-by":"publisher","first-page":"8412","DOI":"10.3390\/s21248412","article-title":"Wearable technology to increase self-awareness of low back pain: a survey of technology needs among health care workers","volume":"21","author":"Ferrone","year":"2021","journal-title":"Sensors (Basel)"},{"key":"B21","doi-asserted-by":"publisher","first-page":"693","DOI":"10.5811\/westjem.58139","article-title":"Applying a smartwatch to predict work-related fatigue for emergency healthcare professionals: machine learning method","volume":"24","author":"Liu","year":"2023","journal-title":"West J Emerg Med"},{"key":"B22","doi-asserted-by":"publisher","first-page":"e36725","DOI":"10.2196\/36725","article-title":"Simulation-based learning supported by technology to enhance critical thinking in nursing students: protocol for a scoping review","volume":"11","author":"Stenseth","year":"2022","journal-title":"JMIR Res Protoc"},{"key":"B23","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.nedt.2016.08.023","article-title":"A systematic review of the effectiveness of simulation-based education on satisfaction and learning outcomes in nurse practitioner programs","volume":"46","author":"Warren","year":"2016","journal-title":"Nurse Educ Today"},{"key":"B24","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1186\/s12912-019-0376-5","article-title":"Nursing students\u2019 transfer of learning outcomes from simulation-based training to clinical practice: a focus-group study","volume":"18","author":"Hustad","year":"2019","journal-title":"BMC Nurs"},{"key":"B25","doi-asserted-by":"publisher","first-page":"22","DOI":"10.5430\/jnep.v12n3p22","article-title":"The effect of mlearning on motivation in the continuing professional development of nursing professionals: a self-determination theory perspective","volume":"12","author":"Sturgeon Delia","year":"2021","journal-title":"J Nurs Educ Pract"},{"key":"B26","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1111\/scs.12743","article-title":"Digital collaborative learning in nursing education: a systematic review","volume":"34","author":"M\u00e4nnist\u00f6","year":"2020","journal-title":"Scand J Caring Sci"},{"key":"B27","doi-asserted-by":"publisher","first-page":"205715851986104","DOI":"10.1177\/2057158519861041","article-title":"Effects of a digital educational intervention on collaborative learning in nursing education: a quasi-experimental study","volume":"39","author":"M\u00e4nnist\u00f6","year":"2019","journal-title":"Nord J Nurs Res"},{"key":"B28","doi-asserted-by":"crossref","DOI":"10.1109\/ICAIS50930.2021.9395906","article-title":"Machine learning based collaborative intelligent closing gap between graduates and labour market framework","author":"Yafooz","year":"2021"},{"key":"B29","first-page":"144","volume-title":"State of the world's nursing 2020: Investing in Education, Jobs and Leadership","author":"McCarthy","year":"2020"},{"key":"B30","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1016\/j.ijnss.2022.06.013","article-title":"Artificial intelligence and end user tools to develop a nurse duty roster scheduling system","volume":"9","author":"Leung","year":"2022","journal-title":"Int J Nurs Sci"},{"key":"B31","doi-asserted-by":"publisher","first-page":"9","DOI":"10.21037\/jhmhp-2020-ai-03","article-title":"Artificial intelligence and healthcare\u2014why they need each other?","volume":"5","author":"Reddy","year":"2021","journal-title":"J Hosp Manag Health Policy"},{"key":"B32","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/s13326-020-00229-7","article-title":"Assisting nurses in care documentation: from automated sentence classification to coherent document structures with subject headings","volume":"11","author":"Moen","year":"2020","journal-title":"J Biomed Semantics"},{"key":"B33","doi-asserted-by":"publisher","first-page":"e26522","DOI":"10.2196\/26522","article-title":"Application scenarios for artificial intelligence in nursing care: rapid review","volume":"23","author":"Seibert","year":"2021","journal-title":"J Med Internet Res"},{"key":"B34","doi-asserted-by":"publisher","first-page":"e1236","DOI":"10.1002\/hpm.2769","article-title":"Using machine-learning methods to support health-care professionals in making admission decisions","volume":"34","author":"Luo","year":"2019","journal-title":"Int J Health Plann Manage"},{"key":"B35","doi-asserted-by":"publisher","first-page":"3802","DOI":"10.1111\/jonm.13736","article-title":"Evolving with technology: machine learning as an opportunity for operating room nurses to improve surgical care-a commentary","volume":"30","author":"Irani","year":"2022","journal-title":"J Nurs Manag"},{"key":"B36","doi-asserted-by":"publisher","first-page":"1834","DOI":"10.1093\/jamia\/ocaa194","article-title":"An approach to predicting patient experience through machine learning and social network analysis","volume":"27","author":"Bari","year":"2020","journal-title":"J Am Med Inform Assoc"},{"key":"B37","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1097\/cin.0000000000000705","article-title":"Data science methods for nursing-relevant patient outcomes and clinical processes: the 2019 literature year in review","volume":"39","author":"Schultz","year":"2021","journal-title":"Comput Inform Nurs"},{"key":"B38","doi-asserted-by":"publisher","first-page":"1415","DOI":"10.1186\/s12913-022-08748-y","article-title":"Effective hospital readmission prediction models using machine-learned features","volume":"22","author":"Davis","year":"2022","journal-title":"BMC Health Serv Res"},{"key":"B39","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1097\/ncq.0000000000000412","article-title":"Leveraging electronic health records and machine learning to tailor nursing care for patients at high risk for readmissions","volume":"35","author":"Brom","year":"2020","journal-title":"J Nurs Care Qual"},{"key":"B40","doi-asserted-by":"publisher","first-page":"022002","DOI":"10.1088\/2516-1091\/abddc5","article-title":"Machine learning in patient flow: a review","volume":"3","author":"El-Bouri","year":"2021","journal-title":"Prog Biomed Eng (Bristol)"},{"key":"B41","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1038\/s42256-020-0176-3","article-title":"Improving healthcare operations management with machine learning","volume":"2","author":"Pianykh","year":"2020","journal-title":"Nat Mach Intell"},{"key":"B42","doi-asserted-by":"crossref","DOI":"10.1109\/NBEC58134.2023.10352582","article-title":"Emerging cloud-based predictive maintenance for hemodialysis reverse osmosis purified water","author":"Amran","year":""},{"key":"B43","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1038\/s41746-023-00805-y","article-title":"An adversarial training framework for mitigating algorithmic biases in clinical machine learning","volume":"6","author":"Yang","year":"2023","journal-title":"npj Digit Med"},{"key":"B44","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1186\/s13054-024-05005-y","article-title":"Should AI models be explainable to clinicians?","volume":"28","author":"Abgrall","year":"2024","journal-title":"Crit Care"},{"key":"B45","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1016\/j.outlook.2022.09.003","article-title":"Algorithmic bias in health care: opportunities for nurses to improve equality in the age of artificial intelligence","volume":"70","author":"O'Connor","year":"2022","journal-title":"Nurs Outlook"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.25172\/smustlr.25.1.3","article-title":"Oculogica: an eye-catching innovation in health care and the privacy implications of artificial intelligence and machine learning in diagnostics for the human brain","volume":"25","author":"Ettari","year":"2022","journal-title":"SMU Sci Technol Law Rev"},{"key":"B47","doi-asserted-by":"publisher","first-page":"884","DOI":"10.1038\/s42256-023-00697-3","article-title":"Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning","volume":"5","author":"Yang","year":"2023","journal-title":"Nat Mach Intell"},{"key":"B48","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1111\/jnu.13007","article-title":"Empowering nurses to champion health equity & be fair: bias elimination for fair and responsible AI in healthcare","volume":"57","author":"Cary","year":"2025","journal-title":"J Nurs Scholarsh"},{"key":"B49","doi-asserted-by":"publisher","first-page":"1921","DOI":"10.23889\/ijpds.v7i3.1921.eCollection2022","article-title":"Ethical considerations in the use of machine learning for research and statistics","volume":"7","author":"Toms","year":"2022","journal-title":"Int J Popul Data Sci"},{"key":"B50","doi-asserted-by":"publisher","first-page":"60","DOI":"10.3390\/informatics10030060","article-title":"Towards a universal privacy model for electronic health record systems: an ontology and machine learning approach","volume":"10","author":"Nowrozy","year":"2023","journal-title":"Informatics"},{"key":"B51","doi-asserted-by":"crossref","DOI":"10.1109\/AIKE55402.2022.00015","article-title":"Ethical and sustainability considerations for knowledge graph based machine learning","author":"Draschner","year":""},{"key":"B52","doi-asserted-by":"publisher","first-page":"20220934","DOI":"10.1259\/bjr.20220934","article-title":"AI and machine learning ethics, law, diversity, and global impact","volume":"96","author":"Drabiak","year":"2023","journal-title":"Br J Radiol"},{"key":"B53","doi-asserted-by":"publisher","DOI":"10.52214\/vib.v7i.8403","article-title":"Legal governance of brain data derived from artificial intelligence","volume":"7","author":"Ahluwalia","year":"2021","journal-title":"Voices Bioeth"},{"key":"B54","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1038\/s41746-022-00700-y","article-title":"Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: a systematic scoping review","volume":"5","author":"Ciecierski-Holmes","year":"2022","journal-title":"npj Digit Med"},{"key":"B55","doi-asserted-by":"publisher","first-page":"1709","DOI":"10.1213\/ane.0000000000004656","article-title":"Machine-learning implementation in clinical anesthesia: opportunities and challenges","volume":"130","author":"Char","year":"2020","journal-title":"Anesth Analg"},{"key":"B56","doi-asserted-by":"publisher","first-page":"e27850","DOI":"10.2196\/27850","article-title":"Chatbot for health care and oncology applications using artificial intelligence and machine learning: systematic review","volume":"7","author":"Xu","year":"2021","journal-title":"JMIR Cancer"},{"key":"B57","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1007\/s10926-020-09895-x","article-title":"Ethical considerations of using machine learning for decision support in occupational health: an example involving periodic workers\u2019 health assessments","volume":"30","author":"Six Dijkstra","year":"2020","journal-title":"J Occup Rehabil"},{"key":"B58","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1007\/978-3-030-85292-4_28","article-title":"Machine learning and ethics","volume":"134","author":"Mathiesen","year":"2022","journal-title":"Acta Neurochir Suppl"},{"key":"B59","doi-asserted-by":"publisher","first-page":"621210","DOI":"10.3389\/fpubh.2021.621210","article-title":"Factors influencing development and implementation of patients\u2019 access to electronic health records-a comparative study of Sweden and The Netherlands","volume":"9","author":"Cijvat","year":"2021","journal-title":"Front Public Health"},{"key":"B60","doi-asserted-by":"publisher","first-page":"939292","DOI":"10.3389\/fdgth.2022.939292","article-title":"Clinical deployment environments: five pillars of translational machine learning for health","volume":"4","author":"Harris","year":"2022","journal-title":"Front Digit Health"},{"key":"B61","doi-asserted-by":"publisher","first-page":"e29301","DOI":"10.2196\/29301","article-title":"Adoption of machine learning systems for medical diagnostics in clinics: qualitative interview study","volume":"23","author":"Pumplun","year":"2021","journal-title":"J Med Internet Res"},{"key":"B62","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1097\/acm.0000000000002661","article-title":"Why we needn't fear the machines: opportunities for medicine in a machine learning world","volume":"94","author":"Li","year":"2019","journal-title":"Acad Med"},{"key":"B63","first-page":"191","article-title":"A review of challenges and opportunities in machine learning for health","author":"Ghassemi","year":"2020"},{"key":"B64","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1108\/MANM-02-2022-0034","article-title":"Application of artificial intelligence: benefits and limitations for human potential and labor-intensive economy\u2014an empirical investigation into pandemic ridden Indian industry","volume":"19","author":"Mukherjee","year":"2022","journal-title":"Manag Matters"},{"key":"B65","doi-asserted-by":"publisher","first-page":"103900","DOI":"10.1016\/j.ijnurstu.2021.103900","article-title":"Interdisciplinary collaboration between nursing and engineering in health care: a scoping review","volume":"117","author":"Zhou","year":"2021","journal-title":"Int J Nurs Stud"},{"key":"B66","doi-asserted-by":"crossref","DOI":"10.2991\/978-94-6463-300-9_43","article-title":"Multidomain big data modeling: concepts and applications","author":"Bao","year":"2023"},{"key":"B67","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1097\/00024665-200405000-00006","article-title":"Data mining as a tool for research and knowledge development in nursing","volume":"22","author":"Berger","year":"2004","journal-title":"Comput Inform Nurs"},{"key":"B68","doi-asserted-by":"publisher","first-page":"269","DOI":"10.2967\/jnmt.119.227819","article-title":"Review of HIPAA, part 1: history, protected health information, and privacy and security rules","volume":"47","author":"Moore","year":"2019","journal-title":"J Nucl Med Technol"},{"key":"B69","doi-asserted-by":"publisher","first-page":"104130","DOI":"10.1016\/j.compbiomed.2020.104130","article-title":"Precision health data: requirements, challenges and existing techniques for data security and privacy","volume":"129","author":"Thapa","year":"2021","journal-title":"Comput Biol Med"},{"key":"B70","doi-asserted-by":"publisher","first-page":"e41588","DOI":"10.2196\/41588","article-title":"Federated machine learning, privacy-enhancing technologies, and data protection laws in medical research: scoping review","volume":"25","author":"Brauneck","year":"2023","journal-title":"J Med Internet Res"},{"key":"B71","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1200\/cci.19.00047","article-title":"Systematic review of privacy-preserving distributed machine learning from federated databases in health care","volume":"4","author":"Zerka","year":"2020","journal-title":"JCO Clin Cancer Inform"},{"key":"B72","doi-asserted-by":"publisher","first-page":"2417","DOI":"10.3390\/ijerph19042417","article-title":"A predictive analysis of heart rates using machine learning techniques","volume":"19","author":"Oyeleye","year":"2022","journal-title":"Int J Environ Res Public Health"},{"key":"B73","doi-asserted-by":"publisher","first-page":"2249","DOI":"10.2139\/ssrn.3522960","article-title":"Reimagining the future of healthcare industry through internet of medical things (IoMT), artificial intelligence (AI), machine learning (ML), big data, Mobile apps and advanced sensors","author":"Narasima Venkatesh","year":"2020","journal-title":"SSRN Electron J"},{"key":"B74","doi-asserted-by":"publisher","first-page":"e46885","DOI":"10.2196\/46885","article-title":"The role of ChatGPT, generative language models, and artificial intelligence in medical education: a conversation with ChatGPT and a call for papers","volume":"9","author":"Eysenbach","year":"2023","journal-title":"JMIR Med Educ"},{"key":"B75","doi-asserted-by":"publisher","first-page":"106544","DOI":"10.1016\/j.nedt.2024.106544","article-title":"Generative artificial intelligence (AI) literacy in nursing education: a crucial call to action","volume":"146","author":"Simms","year":"2025","journal-title":"Nurse Educ Today"},{"key":"B76","doi-asserted-by":"publisher","first-page":"e1001779","DOI":"10.1371\/journal.pmed.1001779","article-title":"UK biobank: an open access resource for identifying the causes of a wide range of Complex diseases of middle and old age","volume":"12","author":"Sudlow","year":"2015","journal-title":"PLoS Med"},{"key":"B77","doi-asserted-by":"publisher","first-page":"12826","DOI":"10.1109\/JIOT.2021.3073904","article-title":"Harnessing the power of smart and connected health to tackle COVID-19: IoT, AI, robotics, and blockchain for a better world","volume":"8","author":"Firouzi","year":"2021","journal-title":"IEEE Internet Things J"},{"key":"B78","doi-asserted-by":"publisher","first-page":"4645","DOI":"10.3390\/ijms23094645","article-title":"Innovations in genomics and big data analytics for personalized medicine and health care: a review","volume":"23","author":"Hassan","year":"2022","journal-title":"Int J Mol Sci"},{"key":"B79","doi-asserted-by":"publisher","first-page":"bbac191","DOI":"10.1093\/bib\/bbac191","article-title":"Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine","volume":"23","author":"Vadapalli","year":"2022","journal-title":"Brief Bioinform"},{"key":"B80","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1186\/s12933-023-01985-3","article-title":"Machine learning in precision diabetes care and cardiovascular risk prediction","volume":"22","author":"Oikonomou","year":"2023","journal-title":"Cardiovasc Diabetol"},{"key":"B81","first-page":"302","article-title":"Artificial intelligence in nursing education: opportunities and challenges","volume":"82","author":"Glauberman","year":"2023","journal-title":"Hawai'i J Health Soc Welf"},{"key":"B82","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1097\/ncc.0000000000001254","article-title":"Application of artificial intelligence in oncology nursing: a scoping review","volume":"47","author":"Zhou","year":"2024","journal-title":"Cancer Nurs"},{"key":"B83","doi-asserted-by":"publisher","first-page":"e70010","DOI":"10.1111\/inr.70010","article-title":"Ethical and regulatory considerations in the use of AI and machine learning in nursing: a systematic review","volume":"72","author":"Mohammed","year":"2025","journal-title":"Int Nurs Rev"},{"key":"B84","doi-asserted-by":"publisher","first-page":"e31043","DOI":"10.2196\/31043","article-title":"Artificial intelligence education programs for health care professionals: scoping review","volume":"7","author":"Charow","year":"2021","journal-title":"JMIR Med Educ"}],"container-title":["Frontiers in Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2025.1514133\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T05:29:09Z","timestamp":1747978149000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2025.1514133\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,23]]},"references-count":84,"alternative-id":["10.3389\/fdgth.2025.1514133"],"URL":"https:\/\/doi.org\/10.3389\/fdgth.2025.1514133","relation":{},"ISSN":["2673-253X"],"issn-type":[{"value":"2673-253X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,23]]},"article-number":"1514133"}}