Citations

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.

Introduction: Ubiquinol–cytochrome c reductase core protein II (UQCRC2) encodes a core subunit of the mitochondrial electron transport chain (ETC) complex III (CIII). Biallelic pathogenic variants in UQCRC2 have been associated with mitochondrial disease characterized by lactic acidosis, developmental delay, hepatopathy, and episodic metabolic decompensation. Methods: We reviewed the biochemical phenotypes of 14 individuals possessing UQCRC2 variants, including two novel cases. We performed biochemical studies of mitochondrial respiration and oxidative phosphorylation (OXPHOS) complex measurements in patient-derived fibroblasts. Results: We report reduced CIII activity in a majority of individuals possessing variants in UQCRC2, as well as biochemical findings consistent with impaired mitochondrial energy metabolism, though impairments in mitochondrial respiration were variable. The two previously unreported, unrelated patients possessing the likely pathogenic missense variant c.361T>C, p.Tyr121His in UQCRC2 in trans with a 16p12.2 microdeletion encompassing UQCRC2 showed milder phenotypes, less severe metabolic decompensations, and no long-term neurological impairments. Both individuals display reduced CIII activity and mitochondrial respiratory dysfunction. Discussion: These data expand the current understanding of genotypes associated with UQCRC2-associated mitochondrial disease to include the novel 16p12.2 microdeletion. These data also highlight the consistent biochemical phenotype associated with UQCRC2-associated mitochondrial disease, and the need for consistent biochemical and respiratory assessment of individuals possessing UQCRC2 variants to further our understanding of this phenotype.

This chapter provides an overview of Mayo Clinic's history, achievements, and supply chain management (SCM) strategies. As a prominent academic medical center in the United States, Mayo Clinic operates major campuses in Minnesota, Florida, and Arizona. The organization manages an annual supply expenditure of $6.8 billion and employs approximately 1,300 SCM staff. Mayo Clinic SCM leading practices encompass a vision, mission, and strategic plan; active engagement of senior leadership; clinical practice–supply chain integration; talent recruitment, development, and retention; partnerships (strategic) and supplier relationship management; commitment to service excellence and continuous improvement; digital information technology (IT) platform and strategy; innovation and automation; benchmarking; and business continuity.

This chapter focuses on key factors that impact effective teamwork in healthcare organizations, including a case study on the applications of a teamwork model at Mayo Clinic in Rochester, MN. Studies have found positive correlations among effective teamwork, patient care, and provider satisfaction. Key factors such as teamwork training, leaderless online group discussion, servant leadership, and top management support lead to effective teamwork, which would positively impact the quadruple aim—patient experience, high-quality and safe delivery of patient care, lower costs, and provider satisfaction.

Several reports show a shortage of primary care physicians in the US, and it seems likely to worsen as the population continues to grow and age. Therefore, it is critical to train the next generation of physicians and healthcare providers to effectively meet the needs of the US population. Alongside this structural challenge, the medical profession is also confronting a cultural one—an erosion of professionalism characterized by declining responsibility, integrity, and communication (Foster, 2019). In teaching and training resident physicians and medical students, some of these skills can be taught, but these skills are usually better “caught” than taught. This chapter presents two physicians' personal experiences in medical education. It discusses the significance of role modeling and best practices for effectively transmitting professionalism, competence, and communication to the next generation of resident physicians, medical students, and healthcare providers.

This chapter provides a high-level overview of clinical informatics (CI) and artificial intelligence (AI) in healthcare. It covers foundational concepts such as electronic health records (EHRs) and data standards, current applications including Clinical Decision Support Systems (CDSS) and ambient scribing/documentation, and near-term directions for both clinicians and patients. The chapter also introduces how foundational CI systems and healthcare data are leveraged to develop AI solutions, ranging from traditional machine learning to large language models, while addressing key issues such as bias, generalizability, and regulatory considerations. Finally, the chapter examines a potential future in which decentralized AI models augment clinicians in their daily work, generate deeper clinical insights, and transform patient care through advanced predictive models and digital twins.

This chapter will explore how telemedicine can increase access to care, address the physician shortage, reduce healthcare costs, support patients in managing chronic diseases, and improve the patient experience. Today, with so much growth and reliance on technology, healthcare reform requires understanding how to incorporate technology to optimize the patient experience and outcomes. This chapter will focus on how best to harness the benefits of technology to provide optimal healthcare in a post-2020 world, from a clinician's perspective.

This chapter explores, analyzes, and discusses the challenges and potential recommendations for the unprecedented physician shortage affecting US healthcare systems. As a result, patients are waiting long periods to get an appointment, and in many cases, even longer to see a physician. Especially older patients who may need immediate attention and medical care end up waiting several weeks. Healthcare seems to be facing a crisis that is getting worse by the day. How did we get here, why, and what do we do about it are some of the questions addressed in this chapter.

Abstract Aims Monitoring pulmonary congestion in chronic heart failure (HF) reduces decompensation and hospitalization, but conventional methods such as weight and symptom tracking are often unreliable. As fluid accumulation affects the lungs and vocal tract, subtle voice alterations may serve as a non-invasive signal for early detection of worsening HF. Methods and results The Voice Analysis for Monitoring Patients with HF trial (VAMP-HF, NCT06566911) prospectively enrolled 104 patients hospitalized with acute decompensated HF (ADHF) across two academic centres in the USA and Germany. Daily voice recordings were collected from admission to discharge, with breathing features extracted from speech and acoustic features from sustained vowels. A machine-learning model was trained to classify recordings as admission-phase vs. discharge-phase using leave-one-patient-out. Patients with clinical deterioration, insufficient audio quality, or short length of stay were excluded. Seventy-nine patients were included in the final dataset. The model classified admission and discharge with an F1-score of 0.83 (95% CI: 0.77–0.90; AUC = 0.90). In patients with higher audio volume (N = 54), performance reached 0.89 (95% CI: 0.82–0.94; AUC = 0.91). When applied to intermediate hospitalization days, model-predicted scores showed progressive increases from admission towards discharge. Performance remained robust irrespective of significant weight loss during hospitalization. Conclusion In this pilot study, structured voice and breathing analysis discriminated hospitalization phase from admission through discharge in patients with ADHF. This non-invasive approach captured progressive changes during the hospital course and warrants further investigation with concurrent objective congestion markers to establish physiological specificity.

Abstract Education and allergy referral via nurse-driven online messaging to patients (n = 1,284) with penicillin allergy labels (PALs) reduced PALs by 8.6%. The percentage decrease of “unknown” (14.8%) and “rash” (7.9%) PALs were higher vs “hives” (6.1%) and “itching” (1.4%) reaction types.