Chapter 52: The Use of Artificial Intelligence in Nephrology: A Paradigm Shift in Kidney Care

Learning Objectives

By the end of this chapter, learners will be able to:

1. Define the major forms of artificial intelligence relevant to nephrology.
2. Describe current applications in AKI detection, CKD progression, dialysis, transplantation, pathology, and imaging.
3. Interpret the strengths and limitations of clinical evidence for nephrology AI systems.
4. Recognize data-quality, bias, regulatory, privacy, workforce, and accountability challenges.
5. Design a safe human-in-the-loop implementation pathway for an AI-supported kidney-care workflow.
6. Discuss emerging roles for multimodal AI, generative AI, wearables, and remote monitoring.

Summary

Chronic kidney disease affects an estimated 850 million people worldwide and creates a substantial burden of morbidity, mortality, and cost. Artificial intelligence (AI) can analyze complex longitudinal data and may support earlier diagnosis, individualized risk prediction, treatment optimization, and precision kidney care. Its value depends on clinically meaningful endpoints, external validation, transparent reporting, secure implementation, and accountable human oversight.

Introduction

AI is emerging across the nephrology pathway, from early detection of kidney injury to management of end-stage kidney disease. This chapter reviews current applications, clinical implementation examples, implementation barriers, and future prospects while emphasizing that predictive performance alone does not establish clinical benefit.

Acute Kidney Injury

AI-powered models have shown promise in predicting AKI before it becomes clinically apparent. The chapter manuscript cites work by Lee and colleagues in which an extreme-gradient-boosting model improved postoperative AKI prediction after cardiac surgery. It also cites the deep-learning model of Tomašev and colleagues, trained on health records from more than 700,000 patients, which reportedly predicted 55% of AKI episodes and 90.2% of dialysis-requiring cases up to 48 hours in advance [1]. These results should be interpreted in context: model transportability, alert burden, prospective benefit, and clinical actionability remain essential questions.

Chronic Kidney Disease

AI may support CKD risk stratification by combining routine laboratory data, eGFR trajectories, comorbidities, medications, and longitudinal clinical records. The Klinrisk model is described as having been validated in CANVAS and CREDENCE datasets, while KidneyIntelX combines biomarkers with clinical data to identify patients at increased risk of rapid kidney-function decline [1,3]. A risk estimate should lead to a defined clinical action—such as medication review, monitoring intensity, referral planning, or shared decision-making—rather than simply adding another score to the medical record.

Dialysis

Dialysis provides repeated, structured observations that create opportunities for AI-supported safety monitoring. Models have been developed to predict intradialytic hypotension, and other systems have been explored for anemia management and hospitalization-risk prediction. Safe use requires a clear alert owner, appropriate thresholds, review of false positives, and evaluation of whether model use improves patient-centered outcomes without increasing workload or inequity.

Kidney Transplantation

In transplantation, AI is being explored for allograft-failure prediction, organ allocation, personalized immunosuppression, and interpretation of transplant imaging and pathology. The iBox prediction system is an example of a tool intended to estimate long-term allograft-failure risk [1]. Because allocation and immunosuppression decisions have major ethical and clinical consequences, any model must remain interpretable, auditable, and subordinate to multidisciplinary transplant judgment.

Renal Pathology and Imaging

Digital pathology combined with AI can segment and quantify glomeruli, tubules, vessels, and other tissue compartments. Convolutional neural networks have been studied for disease classification and prognostic prediction from histopathological features [2]. External validation must account for scanners, staining protocols, image quality, population differences, and disease spectrum. AI should assist pathologists rather than replace expert interpretation.

Clinical Implementation and Real-World Evidence

Translation from research to practice remains early, but the manuscript identifies several implementation examples:

These examples demonstrate feasibility and reported real-world use; they should not be interpreted as universal causal evidence without independent replication, local validation, and prospective evaluation.

Data quality and accessibility

AI models require large, representative, high-quality datasets. Fragmented records, missingness, inconsistent definitions, and differences in laboratory or workflow systems can reduce reliability and transportability.

Regulation, ethics, and privacy

Clinical AI raises questions about bias, transparency, accountability, cybersecurity, data protection, and appropriate regulatory oversight. Governance should define intended use, prohibited use, documentation, change control, and monitoring responsibilities.

Workforce and adoption

Successful implementation requires clinicians and allied professionals who understand the tool’s purpose, uncertainty, and failure modes. AI should augment rather than replace clinical reasoning, and the workflow should specify who reviews alerts and what action follows.

Multimodal AI

Combining laboratory data with imaging, digital pathology, genomics, proteomics, and longitudinal clinical information may enable more complete phenotyping and more personalized treatment strategies.

Generative AI

Generative systems may support clinical documentation, patient communication, medical education, and information retrieval. Their use requires verification, privacy protection, controlled access, and safeguards against fabricated or incomplete clinical information.

Wearables and remote monitoring

AI-supported home measurements and remote monitoring may help detect complications earlier and extend kidney care beyond the clinic. Successful deployment depends on data quality, access, digital inclusion, and a response pathway for abnormal signals.

Clinical Pearls

• Prediction is not diagnosis. A model output must be interpreted in the patient’s clinical context.
• Actionability matters. Every alert should have a defined owner, response, and reassessment plan.
• External validation is essential. Performance can change across populations, hospitals, devices, and laboratory systems.
• Calibration and equity deserve equal attention to discrimination. A model may rank risk well while still producing clinically misleading probabilities or unequal performance.
• Human oversight is a safety requirement. AI should support nephrologists, nurses, pathologists, radiologists, and transplant teams rather than displace them.
• Monitor after deployment. Model drift, alert fatigue, cybersecurity, workflow burden, and unintended harms must be assessed continuously.

Conclusion

AI is poised to influence nephrology across AKI detection, CKD progression prediction, dialysis safety, transplantation, pathology, imaging, and remote care. The most credible path forward is evidence-based and human-centered: define a meaningful clinical problem, validate the model across relevant settings, integrate it into an accountable workflow, monitor its real-world performance, and evaluate whether it improves outcomes for patients with kidney disease.

References

1. Singh P, et al. Artificial Intelligence in Nephrology: Clinical Applications and Challenges. Kidney Medicine. 2025;7(1). Full text.
2. Feng C, Liu F. Artificial intelligence in renal pathology: Current status and future. Biomolecules & Biomedicine. 2023;23(2):225–234. Full text.
3. Tangri N, et al. Machine learning for prediction of chronic kidney disease progression: Validation of the Klinrisk model in the CANVAS Program and CREDENCE trial. Diabetes, Obesity and Metabolism. 2024. Full text.
4. Roche. Roche receives CE Mark for AI-based Kidney Klinrisk Algorithm and launches a comprehensive chronic kidney disease algorithm panel. Press release, 2025. Source.