{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T13:28:52Z","timestamp":1792157332159,"version":"4.4.0"},"reference-count":116,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T00:00:00Z","timestamp":1774569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: Artificial intelligence in nephrology has largely focused on predictive models for outcomes such as acute kidney injury (AKI), chronic kidney disease (CKD) progression, and transplant complications. Although these models demonstrate technical performance, their real-world clinical impact has remained limited because prediction alone rarely translates into coordinated clinical action. Clinical artificial intelligence agents represent workflow-native systems that operate in real time, interact bidirectionally with clinical environments, adapt to evolving patient and workflow states, and support coordinated clinical action rather than generating isolated predictions. This review proposes clinical artificial intelligence agents as a new paradigm for integrating artificial intelligence directly into nephrology workflows. Methods: We conducted a narrative synthesis of emerging literature on artificial intelligence systems, agentic artificial intelligence architectures, clinical decision support, and digital health infrastructures relevant to kidney care. Drawing from interdisciplinary sources in medicine, health informatics, and artificial intelligence research, we developed a conceptual framework describing the architecture, governance requirements, and evaluation principles of clinical artificial intelligence agents in nephrology. Results: Clinical artificial intelligence agents represent workflow-integrated systems capable of continuously perceiving patient data, reasoning under clinical constraints, planning tasks, and supporting coordinated clinical actions over time. We describe a layered architecture consisting of perception, cognition, planning and control, action, and learning components. Potential applications span the nephrology care continuum, including CKD management, AKI monitoring, dialysis and continuous renal replacement therapy (CRRT) optimization, kidney transplantation care coordination, glomerulonephritis management, and supervised patient-facing systems. Conclusions: Clinical artificial intelligence agents shift the role of artificial intelligence from isolated prediction toward longitudinal clinical orchestration. Future evaluation should prioritize workflow integration, time-to-action, clinician oversight, safety, and patient-centered outcomes rather than relying solely on traditional model performance metrics. This roadmap provides a conceptual foundation for the responsible development and clinical integration of agentic artificial intelligence systems in nephrology.<\/jats:p>","DOI":"10.3390\/jcm15072576","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T14:33:33Z","timestamp":1774622013000},"page":"2576","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Clinical Artificial Intelligence Agents in Nephrology: From Prediction to Action Through Workflow-Native Intelligence\u2014A Roadmap for Workflow-Integrated Care"],"prefix":"10.3390","volume":"15","author":[{"given":"Charat","family":"Thongprayoon","sequence":"first","affiliation":[{"name":"Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2882-4226","authenticated-orcid":false,"given":"Francesco","family":"Pesce","sequence":"additional","affiliation":[{"name":"Division of Renal Medicine, Ospedale Isola Tiberina-Gemelli Isola, 00168 Rome, Italy"},{"name":"Department of Translational Medicine and Surgery, Universit\u00e0 Cattolica del Sacro Cuore, 00168 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9954-9711","authenticated-orcid":false,"given":"Wisit","family":"Cheungpasitporn","sequence":"additional","affiliation":[{"name":"Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2461","DOI":"10.1016\/S0140-6736(25)01853-7","article-title":"Global, regional, and national burden of chronic kidney disease in adults, 1990\u20132023, and its attributable risk factors: A systematic analysis for the Global Burden of Disease Study 2023","volume":"406","author":"Mark","year":"2025","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/S0140-6736(20)30045-3","article-title":"Global, regional, and national burden of chronic kidney disease, 1990\u20132017: A systematic analysis for the Global Burden of Disease Study 2017","volume":"395","author":"Bikbov","year":"2020","journal-title":"Lancet"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hill, N.R., Fatoba, S.T., Oke, J.L., Hirst, J.A., O\u2019Callaghan, C.A., Lasserson, D.S., and Hobbs, F.D. (2016). Global Prevalence of Chronic Kidney Disease\u2014A Systematic Review and Meta-Analysis. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0158765"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Tangri, N., Cheungpasitporn, W., Crittenden, S.D., Fornoni, A., Peralta, C.A., Singh, K., Usvyat, L.A., and Waterman, A.D. (2025). Responsible Use of Artificial Intelligence to Improve Kidney Care: A Statement from the American Society of Nephrology. J. Am. Soc. Nephrol.","DOI":"10.1681\/ASN.0000000929"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1038\/s41581-022-00562-3","article-title":"Artificial intelligence-enabled decision support in nephrology","volume":"18","author":"Loftus","year":"2022","journal-title":"Nat. Rev. Nephrol."},{"key":"ref_6","first-page":"533","article-title":"Advances in critical care nephrology through artificial intelligence","volume":"30","author":"Cheungpasitporn","year":"2024","journal-title":"Curr. Opin. Crit. Care"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"151673","DOI":"10.1016\/j.semnephrol.2025.151673","article-title":"Using Predictive Models and AI for AKI Research","volume":"46","author":"Rajagopal","year":"2025","journal-title":"Semin. Nephrol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1097\/MNH.0000000000000787","article-title":"Role of artificial intelligence in the diagnosis and management of kidney disease: Applications to chronic kidney disease and acute kidney injury","volume":"31","author":"Tangri","year":"2022","journal-title":"Curr. Opin. Nephrol. Hypertens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4566","DOI":"10.7150\/thno.108552","article-title":"Artificial intelligence in chronic kidney disease management: A scoping review","volume":"15","author":"Sabanayagam","year":"2025","journal-title":"Theranostics"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1053\/j.ajkd.2019.05.020","article-title":"Artificial Intelligence in Nephrology: Core Concepts, Clinical Applications, and Perspectives","volume":"74","author":"Niel","year":"2019","journal-title":"Am. J. Kidney Dis."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"17562872251352553","DOI":"10.1177\/17562872251352553","article-title":"Artificial intelligence and radiomics applications in adrenal lesions: A systematic review","volume":"17","author":"Ferro","year":"2025","journal-title":"Ther. Adv. Urol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1097\/MNH.0000000000001132","article-title":"Artificial intelligence in kidney disease and dialysis: From data mining to clinical impact","volume":"35","author":"Neri","year":"2026","journal-title":"Curr. Opin. Nephrol. Hypertens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1097\/MNH.0000000000001112","article-title":"Artificial intelligence at the bedside for the prevention, detection, and management of acute kidney injury","volume":"34","author":"Shickel","year":"2025","journal-title":"Curr. Opin. Nephrol. Hypertens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1007\/s40620-023-01573-4","article-title":"Predict, diagnose, and treat chronic kidney disease with machine learning: A systematic literature review","volume":"36","author":"Sanmarchi","year":"2023","journal-title":"J. Nephrol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"S117","DOI":"10.1016\/j.kint.2023.10.018","article-title":"KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease","volume":"105","author":"Stevens","year":"2024","journal-title":"Kidney Int."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e2225797","DOI":"10.1001\/jamanetworkopen.2022.25797","article-title":"Analysis of Specialty Nephrology Care Among Patients With Chronic Kidney Disease and High Risk of Disease Progression","volume":"5","author":"Wang","year":"2022","journal-title":"JAMA Netw. Open"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1097\/MNH.0000000000000950","article-title":"Predictive models in chronic kidney disease: Essential tools in clinical practice","volume":"33","author":"Spasiano","year":"2024","journal-title":"Curr. Opin. Nephrol. Hypertens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"664","DOI":"10.34067\/KID.0000001037","article-title":"Artificial Intelligence in Critical Care Nephrology: Current Applications, Emerging Techniques, and Challenges to Clinical Integration","volume":"7","author":"Cheungpasitporn","year":"2025","journal-title":"Kidney360"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"sfag004","DOI":"10.1093\/ckj\/sfag004","article-title":"Transforming nephrology through artificial intelligence: A state-of-the-art roadmap for clinical integration","volume":"19","author":"Cheungpasitporn","year":"2026","journal-title":"Clin. Kidney J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"F395","DOI":"10.1152\/ajprenal.00177.2023","article-title":"\u201cHi, how can i help you?\u201d: Embracing artificial intelligence in kidney research","volume":"325","author":"Layton","year":"2023","journal-title":"Am. J. Physiol. Ren. Physiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1159\/000548208","article-title":"Artificial Intelligence-Driven Nephrology: The Role of Large Language Models in Kidney Care","volume":"28","author":"Zoccali","year":"2025","journal-title":"Am. J. Nephrol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1053\/j.ajkd.2024.08.003","article-title":"KDOQI US Commentary on the KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of CKD","volume":"85","author":"Navaneethan","year":"2025","journal-title":"Am. J. Kidney Dis."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1001\/jama.2024.16338","article-title":"Dialysis for Chronic Kidney Failure: A Review","volume":"332","author":"Flythe","year":"2024","journal-title":"JAMA"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"S239","DOI":"10.2337\/dc25-S011","article-title":"11. Chronic Kidney Disease and Risk Management: Standards of Care in Diabetes-2025","volume":"48","author":"ElSayed","year":"2025","journal-title":"Diabetes Care"},{"key":"ref_25","first-page":"687","article-title":"Acute Kidney Injury: Diagnosis and Management","volume":"100","author":"Mercado","year":"2019","journal-title":"Am. Fam. Physician"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1053\/j.ajkd.2017.11.021","article-title":"Management of Acute Kidney Injury: Core Curriculum 2018","volume":"72","author":"Moore","year":"2018","journal-title":"Am. J. Kidney Dis."},{"key":"ref_27","first-page":"631","article-title":"Acute kidney injury: A guide to diagnosis and management","volume":"86","author":"Rahman","year":"2012","journal-title":"Am. Fam. Physician"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1016\/j.chest.2019.04.004","article-title":"Fluid Management in Acute Kidney Injury","volume":"156","author":"Ostermann","year":"2019","journal-title":"Chest"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/S0140-6736(13)60647-9","article-title":"Acute kidney injury: An increasing global concern","volume":"382","author":"Lameire","year":"2013","journal-title":"Lancet"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1097\/MNH.0000000000000921","article-title":"Electronic alerts in acute kidney injury: Why does evidence of benefit remain elusive?","volume":"32","author":"Horne","year":"2023","journal-title":"Curr. Opin. Nephrol. Hypertens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2081","DOI":"10.1001\/jama.2024.22718","article-title":"Early, Individualized Recommendations for Hospitalized Patients With Acute Kidney Injury: A Randomized Clinical Trial","volume":"332","author":"Aklilu","year":"2024","journal-title":"JAMA"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.kint.2023.10.001","article-title":"Conservative kidney management and kidney supportive care: Core components of integrated care for people with kidney failure","volume":"105","author":"Davison","year":"2024","journal-title":"Kidney Int."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/S0140-6736(13)60438-9","article-title":"Kidney failure: Aims for the next 10 years and barriers to success","volume":"382","author":"Remuzzi","year":"2013","journal-title":"Lancet"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1053\/j.ajkd.2020.01.001","article-title":"The Role of the General Nephrologist in Evaluating Patients for Kidney Transplantation: Core Curriculum 2020","volume":"76","author":"Virmani","year":"2020","journal-title":"Am. J. Kidney Dis."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.2215\/CJN.0000000000000441","article-title":"Should Transplant Nephrology Pursue Recognition from the Accreditation Council for Graduate Medical Education (ACGME)?","volume":"19","author":"Singh","year":"2024","journal-title":"Clin. J. Am. Soc. Nephrol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1053\/j.ackd.2020.05.005","article-title":"The Transplant Nephrology Workforce in the United States: Current State and Future Directions","volume":"27","author":"Concepcion","year":"2020","journal-title":"Adv. Chronic Kidney Dis."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"ii43","DOI":"10.1093\/ndt\/gfae126","article-title":"A global assessment of kidney care workforce","volume":"39","author":"Okpechi","year":"2024","journal-title":"Nephrol. Dial. Transpl."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1053\/j.ackd.2020.07.007","article-title":"Current State of the Workforce in Nephrology","volume":"27","author":"Lederer","year":"2020","journal-title":"Adv. Chronic Kidney Dis."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1864","DOI":"10.1001\/jama.2017.4046","article-title":"Assessment of Global Kidney Health Care Status","volume":"317","author":"Bello","year":"2017","journal-title":"JAMA"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Piccoli, G.B., Breuer, C., Cabiddu, G., Testa, A., Jadeau, C., and Brunori, G. (2018). Where Are You Going, Nephrology? Considerations on Models of Care in an Evolving Discipline. J. Clin. Med., 7.","DOI":"10.3390\/jcm7080199"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1093\/jamia\/ocaa294","article-title":"Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions","volume":"28","author":"Morris","year":"2021","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1053\/j.ackd.2007.10.005","article-title":"Automation, decision support, and expert systems in nephrology","volume":"15","author":"Soman","year":"2008","journal-title":"Adv. Chronic Kidney Dis."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1111\/sdi.12915","article-title":"Artificial intelligence enabled applications in kidney disease","volume":"34","author":"Chaudhuri","year":"2021","journal-title":"Semin. Dial."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1186\/s40001-025-03196-w","article-title":"Artificial intelligence in healthcare and medicine: Clinical applications, therapeutic advances, and future perspectives","volume":"30","author":"Fahim","year":"2025","journal-title":"Eur. J. Med. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e67485","DOI":"10.2196\/67485","article-title":"AI for IMPACTS Framework for Evaluating the Long-Term Real-World Impacts of AI-Powered Clinician Tools: Systematic Review and Narrative Synthesis","volume":"27","author":"Jacob","year":"2025","journal-title":"J. Med. Internet Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1650","DOI":"10.1001\/jama.2025.18490","article-title":"AI, Health, and Health Care Today and Tomorrow: The JAMA Summit Report on Artificial Intelligence","volume":"334","author":"Angus","year":"2025","journal-title":"JAMA"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"e66100","DOI":"10.2196\/66100","article-title":"AI and Machine Learning Terminology in Medicine, Psychology, and Social Sciences: Tutorial and Practical Recommendations","volume":"27","author":"Cao","year":"2025","journal-title":"J. Med. Internet Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1001\/jamainternmed.2025.4914","article-title":"How Physicians Can Prepare for Generative AI","volume":"185","author":"Morgan","year":"2025","journal-title":"JAMA Intern. Med."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Gruson, D., Gouget, B., Lee, W., Greaves, R., Liu, Y., Ebert, S., Yang, H.S., and Shah, S. (2025). From automation to agentic artificial intelligence in laboratory medicine: An opinion of the IFCC Division on Emerging Technologies. Clin. Chem. Lab. Med.","DOI":"10.1515\/cclm-2025-1314"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4475","DOI":"10.1002\/ksa.70109","article-title":"Artificial intelligence agents in orthopaedics: Concepts, capabilities and the road ahead","volume":"33","author":"Oettl","year":"2025","journal-title":"Knee Surg. Sports Traumatol. Arthrosc."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1001\/jama.2023.25057","article-title":"Three Epochs of Artificial Intelligence in Health Care","volume":"331","author":"Howell","year":"2024","journal-title":"JAMA"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1093\/eurheartj\/ehac758","article-title":"Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare","volume":"44","author":"Gill","year":"2023","journal-title":"Eur. Heart J."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1055\/s-0040-1719037","article-title":"Artificial Intelligence in the Intensive Care Unit","volume":"42","author":"Greco","year":"2021","journal-title":"Semin. Respir. Crit. Care Med."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Tasmurzayev, N., Amangeldy, B., Imanbek, B., Baigarayeva, Z., Imankulov, T., Dikhanbayeva, G., Amangeldi, I., and Sharipova, S. (2025). Digital Cardiovascular Twins, AI Agents, and Sensor Data: A Narrative Review from System Architecture to Proactive Heart Health. Sensors, 25.","DOI":"10.3390\/s25175272"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1097\/ACM.0000000000003707","article-title":"Artificial Intelligence and Clinical Decision Making: The New Nature of Medical Uncertainty","volume":"96","author":"Harish","year":"2021","journal-title":"Acad. Med."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"106014","DOI":"10.1016\/j.ijmedinf.2025.106014","article-title":"Modeling unknowns: A vision for uncertainty-aware machine learning in healthcare","volume":"203","author":"Campagner","year":"2025","journal-title":"Int. J. Med. Inform."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"4950","DOI":"10.1016\/j.arthro.2025.05.021","article-title":"Commercial Products Using Generative Artificial Intelligence Include Ambient Scribes, Automated Documentation and Scheduling, Revenue Cycle Management, Patient Engagement and Education, and Prior Authorization Platforms","volume":"41","author":"Kunze","year":"2025","journal-title":"Arthroscopy"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2635","DOI":"10.2174\/0115680266339394250225112747","article-title":"The Impact and Role of Artificial Intelligence (AI) in Healthcare: Systematic Review","volume":"25","author":"Singh","year":"2025","journal-title":"Curr. Top. Med. Chem."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"210","DOI":"10.3122\/jabfm.2022.220171R1","article-title":"Priorities for Artificial Intelligence Applications in Primary Care: A Canadian Deliberative Dialogue with Patients, Providers, and Health System Leaders","volume":"36","author":"Upshaw","year":"2023","journal-title":"J. Am. Board. Fam. Med."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"e256359","DOI":"10.1001\/jamanetworkopen.2025.6359","article-title":"Semantic Clinical Artificial Intelligence vs Native Large Language Model Performance on the USMLE","volume":"8","author":"Elkin","year":"2025","journal-title":"JAMA Netw. Open"},{"key":"ref_61","first-page":"16","article-title":"Challenges of Artificial Intelligence in Medicine","volume":"323","author":"Aldosari","year":"2025","journal-title":"Stud. Health Technol. Inform."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"44432","DOI":"10.1038\/s41598-025-25266-z","article-title":"A validated framework for responsible AI in healthcare autonomous systems","volume":"15","author":"Alelyani","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1001\/jama.2024.21700","article-title":"Testing and Evaluation of Health Care Applications of Large Language Models: A Systematic Review","volume":"333","author":"Bedi","year":"2025","journal-title":"JAMA"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"El Arab, R.A., Al Moosa, O.A., and Sagbakken, M. (2025). Economic, ethical, and regulatory dimensions of artificial intelligence in healthcare: An integrative review. Front. Public Health, 13.","DOI":"10.3389\/fpubh.2025.1617138"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.1093\/bjr\/tqaf173","article-title":"Agentic AI in radiology: Emerging potential and unresolved challenges","volume":"98","author":"Dietrich","year":"2025","journal-title":"Br. J. Radiol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"e40238","DOI":"10.2196\/40238","article-title":"Artificial Intelligence Applications in Health Care Practice: Scoping Review","volume":"24","author":"Sharma","year":"2022","journal-title":"J. Med. Internet Res."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1001\/jamainternmed.2024.0708","article-title":"Electronic Health Record Population Health Management for Chronic Kidney Disease Care: A Cluster Randomized Clinical Trial","volume":"184","author":"Jhamb","year":"2024","journal-title":"JAMA Intern. Med."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"120749","DOI":"10.1016\/j.cca.2025.120749","article-title":"Digital health integration in chronic kidney disease","volume":"582","author":"Deng","year":"2025","journal-title":"Clin. Chim. Acta"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"22091","DOI":"10.1038\/s41598-023-49271-2","article-title":"Prediction of chronic kidney disease progression using recurrent neural network and electronic health records","volume":"13","author":"Zhu","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"e48320","DOI":"10.2196\/48320","article-title":"The Use of Deep Learning and Machine Learning on Longitudinal Electronic Health Records for the Early Detection and Prevention of Diseases: Scoping Review","volume":"26","author":"Swinckels","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Hsu, C.T., Pai, K.C., Chen, L.C., Lin, S.H., and Wu, M.J. (2023). Machine Learning Models to Predict the Risk of Rapidly Progressive Kidney Disease and the Need for Nephrology Referral in Adult Patients with Type 2 Diabetes. Int. J. Environ. Res. Public Health, 20.","DOI":"10.3390\/ijerph20043396"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"e54206","DOI":"10.2196\/54206","article-title":"Long-Term Efficacy of an AI-Based Health Coaching Mobile App in Slowing the Progression of Nondialysis-Dependent Chronic Kidney Disease: Retrospective Cohort Study","volume":"26","author":"Ma","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"e45531","DOI":"10.2196\/45531","article-title":"Improving Kidney Outcomes in Patients With Nondiabetic Chronic Kidney Disease Through an Artificial Intelligence-Based Health Coaching Mobile App: Retrospective Cohort Study","volume":"11","author":"Liu","year":"2023","journal-title":"JMIR Mhealth Uhealth"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1097\/CCM.0000000000003123","article-title":"The Development of a Machine Learning Inpatient Acute Kidney Injury Prediction Model","volume":"46","author":"Koyner","year":"2018","journal-title":"Crit. Care Med."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Alfieri, F., Ancona, A., Tripepi, G., Rubeis, A., Arjoldi, N., Finazzi, S., Cauda, V., and Fagugli, R.M. (2023). Continuous and early prediction of future moderate and severe Acute Kidney Injury in critically ill patients: Development and multi-centric, multi-national external validation of a machine-learning model. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0287398"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"9313","DOI":"10.1038\/s41598-025-90459-5","article-title":"Construction of a machine learning-based interpretable prediction model for acute kidney injury in hospitalized patients","volume":"15","author":"Yu","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1080\/10408363.2025.2497843","article-title":"Machine learning models for acute kidney injury prediction and management: A scoping review of externally validated studies","volume":"62","author":"Rehman","year":"2025","journal-title":"Crit. Rev. Clin. Lab. Sci."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"e66568","DOI":"10.2196\/66568","article-title":"Machine Learning to Assist in Managing Acute Kidney Injury in General Wards: Multicenter Retrospective Study","volume":"27","author":"Cho","year":"2025","journal-title":"J. Med. Internet Res."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"2310","DOI":"10.1093\/ndt\/gfad064","article-title":"Prediction of intradialytic hypotension using pre-dialysis features\u2014A deep learning-based artificial intelligence model","volume":"38","author":"Lee","year":"2023","journal-title":"Nephrol. Dial. Transpl."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"120908","DOI":"10.1016\/j.cca.2026.120908","article-title":"Artificial intelligence and machine learning applications in dialysis: Current applications, challenges, and future directions","volume":"586","author":"Ogunbona","year":"2026","journal-title":"Clin. Chim. Acta"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"e27098","DOI":"10.2196\/27098","article-title":"Machine Learning Analysis of Time-Dependent Features for Predicting Adverse Events During Hemodialysis Therapy: Model Development and Validation Study","volume":"23","author":"Liu","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"4605","DOI":"10.1038\/s41598-023-30074-4","article-title":"Predicting outcomes of continuous renal replacement therapy using body composition monitoring: A deep-learning approach","volume":"13","author":"Yoo","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"2001","DOI":"10.1007\/s40620-023-01741-6","article-title":"Development and validation of AI-based triage support algorithms for prevention of intradialytic hypotension","volume":"36","author":"Gervasoni","year":"2023","journal-title":"J. Nephrol."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1186\/s12967-025-06488-1","article-title":"An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation","volume":"23","author":"Arjmandmazidi","year":"2025","journal-title":"J. Transl. Med."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Peloso, A., Moeckli, B., Delaune, V., Oldani, G., Andres, A., and Compagnon, P. (2022). Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation. Transpl. Int., 35.","DOI":"10.3389\/ti.2022.10640"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"103530","DOI":"10.1016\/j.eclinm.2025.103530","article-title":"Artificial intelligence-assisted diagnosis of glomerular nephritis using a pathological image analysis approach: A multicentre model development and validation study","volume":"89","author":"Nie","year":"2025","journal-title":"EClinicalMedicine"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"2533","DOI":"10.1007\/s00467-021-05419-8","article-title":"Artificial intelligence in glomerular diseases","volume":"37","author":"Schena","year":"2022","journal-title":"Pediatr. Nephrol."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"e20346","DOI":"10.2196\/20346","article-title":"The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review","volume":"22","author":"Lim","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Du, Y., Yang, P., Liu, Y., Deng, C., and Li, X. (2025). Artificial intelligence in chronic disease self-management: Current applications and future directions. Front. Public Health, 13.","DOI":"10.3389\/fpubh.2025.1689911"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1001\/jama.2025.23059","article-title":"New Guidance on Responsible Use of AI","volume":"335","author":"Palmieri","year":"2025","journal-title":"JAMA"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"4808","DOI":"10.1093\/eurheartj\/ehae619","article-title":"Cardiovascular care with digital twin technology in the era of generative artificial intelligence","volume":"45","author":"Thangaraj","year":"2024","journal-title":"Eur. Heart J."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"e55015","DOI":"10.2196\/55015","article-title":"Digital Twins for Clinical and Operational Decision-Making: Scoping Review","volume":"27","author":"Riahi","year":"2025","journal-title":"J. Med. Internet Res."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1038\/s41746-022-00694-7","article-title":"Health digital twins as tools for precision medicine: Considerations for computation, implementation, and regulation","volume":"5","author":"Venkatesh","year":"2022","journal-title":"NPJ Digit. Med."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1038\/s41746-025-01910-w","article-title":"A scoping review of human digital twins in healthcare applications and usage patterns","volume":"8","author":"Tudor","year":"2025","journal-title":"NPJ Digit. Med."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1146\/annurev-pharmtox-022123-022046","article-title":"Health Digital Twins in Life Science and Health Care Innovation","volume":"64","author":"Venkatesh","year":"2024","journal-title":"Annu. Rev. Pharmacol. Toxicol."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1899","DOI":"10.1093\/jamia\/ocaf152","article-title":"Development and application of desiderata for automated clinical ordering","volume":"32","author":"Saleh","year":"2025","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"e235514","DOI":"10.1001\/jamahealthforum.2023.5514","article-title":"Patient Safety and Artificial Intelligence in Clinical Care","volume":"5","author":"Ratwani","year":"2024","journal-title":"JAMA Health Forum"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"e81184","DOI":"10.2196\/81184","article-title":"AI Act Compliance Within the MyHealth@EU Framework: Tutorial","volume":"27","author":"Shahpaski","year":"2025","journal-title":"J. Med. Internet Res."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1001\/jama.2024.21451","article-title":"FDA Perspective on the Regulation of Artificial Intelligence in Health Care and Biomedicine","volume":"333","author":"Warraich","year":"2025","journal-title":"JAMA"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1080\/17434440.2025.2561918","article-title":"Software as Medical Devices: Requirements and regulatory landscape in the United States","volume":"22","author":"Shah","year":"2025","journal-title":"Expert Rev. Med. Devices"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.semnephrol.2021.05.009","article-title":"A Call for an Ethics and Governance Action Plan to Harness the Power of Artificial Intelligence and Digitalization in Nephrology","volume":"41","author":"Ho","year":"2021","journal-title":"Semin. Nephrol."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1148\/radiol.220182","article-title":"Methods for Clinical Evaluation of Artificial Intelligence Algorithms for Medical Diagnosis","volume":"306","author":"Park","year":"2023","journal-title":"Radiology"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"100916","DOI":"10.1016\/j.landig.2025.100916","article-title":"Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: Overview and guidance","volume":"7","author":"Collins","year":"2025","journal-title":"Lancet Digit. Health"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1093\/jamia\/ocae301","article-title":"AI as an intervention: Improving clinical outcomes relies on a causal approach to AI development and validation","volume":"32","author":"Joshi","year":"2025","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"2120","DOI":"10.1056\/NEJMra2201153","article-title":"Uses of GFR and Albuminuria Level in Acute and Chronic Kidney Disease","volume":"386","author":"Levey","year":"2022","journal-title":"N. Engl. J. Med."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.kint.2023.03.037","article-title":"Effects of newer kidney protective agents on kidney endpoints provide implications for future clinical trials","volume":"104","author":"Heerspink","year":"2023","journal-title":"Kidney Int."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Guan, H., Bates, D., and Zhou, L. (2025). Keeping Medical AI Healthy and Trustworthy: A Review of Detection and Correction Methods for System Degradation. IEEE Trans. Biomed. Eng.","DOI":"10.1109\/TBME.2025.3642706"},{"key":"ref_108","first-page":"343","article-title":"Handoffs and Teamwork: A Framework for Care Transition Communication","volume":"48","author":"Webster","year":"2022","journal-title":"Jt. Comm. J. Qual. Patient Saf."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1056\/NEJMra2503232","article-title":"Educational Strategies for Clinical Supervision of Artificial Intelligence Use","volume":"393","author":"Abdulnour","year":"2025","journal-title":"N. Engl. J. Med."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"e433","DOI":"10.1161\/CIR.0000000000001400","article-title":"Pragmatic Approaches to the Evaluation and Monitoring of Artificial Intelligence in Health Care: A Science Advisory From the American Heart Association","volume":"152","author":"Jain","year":"2025","journal-title":"Circulation"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"104550","DOI":"10.1016\/j.jbi.2023.104550","article-title":"Theory of trust and acceptance of artificial intelligence technology (TrAAIT): An instrument to assess clinician trust and acceptance of artificial intelligence","volume":"148","author":"Stevens","year":"2023","journal-title":"J. Biomed. Inform."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"e69678","DOI":"10.2196\/69678","article-title":"Trust in Artificial Intelligence-Based Clinical Decision Support Systems Among Health Care Workers: Systematic Review","volume":"27","author":"Tun","year":"2025","journal-title":"J. Med. Internet Res."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"102479","DOI":"10.1016\/j.eclinm.2024.102479","article-title":"Health equity assessment of machine learning performance (HEAL): A framework and dermatology AI model case study","volume":"70","author":"Schaekermann","year":"2024","journal-title":"EClinicalMedicine"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"e2513685","DOI":"10.1001\/jamanetworkopen.2025.13685","article-title":"Detecting and Remediating Harmful Data Shifts for the Responsible Deployment of Clinical AI Models","volume":"8","author":"Subasri","year":"2025","journal-title":"JAMA Netw. Open"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1111\/risa.14273","article-title":"Managing risk and resilience in autonomous and intelligent systems: Exploring safety in the development, deployment, and use of artificial intelligence in healthcare","volume":"45","author":"Macrae","year":"2025","journal-title":"Risk Anal."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"106015","DOI":"10.1016\/j.ijmedinf.2025.106015","article-title":"Toward responsible AI governance: Balancing multi-stakeholder perspectives on AI in healthcare","volume":"203","author":"Rozenblit","year":"2025","journal-title":"Int. J. Med. Inform."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/7\/2576\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T04:17:34Z","timestamp":1774930654000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/7\/2576"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,27]]},"references-count":116,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["jcm15072576"],"URL":"https:\/\/doi.org\/10.3390\/jcm15072576","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,27]]}}}