Koyner, Jay L

Scientific evidence regarding AKI and acute kidney care has advanced in recent years. However, a knowledge gap remains on the implementation of these evidence-based practices (EBPs) into clinical care. Implementation science (IS) is focused on ensuring that this knowledge is translated in an effective, efficient and sustainable fashion. Objectives: 1) Define the current status of IS; 2) Define a roadmap for accelerating IS with a focus on patient/care partner advocacy, digital tool application, social determinants of health, and resource-limited settings; and 3) Develop a robust and broad research agenda incorporating IS methodology into the programs. Evidence Review: ADQI XXXV was conducted through a modified Delphi process with virtual meetings preceding an in-person meeting. Five workgroups were determined a priori to focus on 1) IS definitions and methods applicable to AKI and acute kidney care; 2) IS literature in AKI and acute kidney care; 3) Innovations in IS to accelerate the adoption, adherence to, and sustainment of EBPs in AKI and acute kidney care; 4) IS methods in resource-limited settings; and 5) Recommendations to identify and evaluate EBPs that are ready for implementation or de-implementation. Prior to the in-person meeting, each workgroup met virtually to review the literature and develop framework questions to address their objectives. During the two-day in person meeting, through iterative discussions, questions and supporting statements were finalized. These questions and statements were agreed upon through voting to achieve consensus, defined as agreement of ≥80%. Conclusions and Relevance: We report a structured multidisciplinary consensus for defining the role of IS in AKI and acute kidney care. Future programs should address these consensus questions and apply these statements along with IS methodology in the translation of science into clinical practice and the implementation/de-implementation of EBPs in clinical care.

ABSTRACT Artificial intelligence (AI), encompassing machine learning, deep learning and generative AI, is poised to redefine nephrology by enabling earlier detection, more precise risk stratification and workflow-integrated clinical decision support across the spectrum of kidney disease. This state-of-the-art review synthesizes emerging applications of AI in acute kidney injury (AKI), chronic kidney disease (CKD), dialysis and kidney transplantation, with attention to clinical integration, real-world validation, workflow implementation and translational challenges. In AKI, predictive models trained on high-frequency electronic health record data and intensive care unit telemetry have demonstrated strong performance in forecasting critical events, yet translation into routine clinical workflows remains limited. In CKD, machine learning tools support progression risk stratification and phenotype clustering, with the potential to inform individualized surveillance and therapy. AI-enabled dialysis management systems optimize ultrafiltration, anemia control and vascular access surveillance, while generative AI and large language models offer novel capabilities for clinical documentation, triage and patient education. In transplantation, AI applications span organ allocation, dynamic graft monitoring and digital pathology-assisted rejection classification, with validated tools such as the iBox system gaining regulatory recognition. Implementation challenges include data heterogeneity, bias, interpretability, regulatory uncertainty and workflow integration. Looking ahead, multimodal integration of imaging, pathology and multi-omics data may support biologically informed precision nephrology. Reinforcement learning, digital twins and ambient intelligence are emerging as adaptive decision-support paradigms rather than autonomous care systems. Regulatory frameworks are evolving to accommodate adaptive algorithms, underscoring the need for clinician engagement in model development, validation and deployment. As AI matures from pilot to practice, nephrologists who embrace and help shape these tools will lead the transition toward a more personalized, efficient and equitable future for kidney care.