Large Language Models and Generative AI in Nephrology: From Clinical Decision Support to Real-World Practice

Artificial intelligence (AI) has entered a transformative phase with the emergence of large language models (LLMs) and generative AI. These technologies are rapidly moving beyond experimental applications and are beginning to influence clinical practice, education, research, and healthcare delivery. In nephrology, where clinical decisions often require integration of complex laboratory data, imaging, pathology, dialysis prescriptions, medications, comorbidities, and longitudinal follow-up, LLMs have the potential to become valuable assistants that support clinicians rather than replace them.Recent advances have demonstrated promising applications of generative AI in clinical decision support, risk stratification, guideline interpretation, patient education, scientific writing, electronic health record summarization, and workflow optimization. Simultaneously, machine learning models continue to improve prediction of kidney disease progression, acute kidney injury, dialysis outcomes, graft survival, cardiovascular complications, and mortality. Together, predictive AI and generative AI are reshaping the future of kidney care.Despite this enthusiasm, substantial challenges remain. Questions regarding reliability, hallucinations, bias, explainability, transparency, data privacy, cybersecurity, regulatory oversight, and responsible implementation must be addressed before widespread clinical adoption. High-quality validation studies, implementation research, and multidisciplinary collaboration are therefore essential.By bringing together nephrologists, transplant physicians, intensivists, internists, data scientists, biomedical engineers, ethicists, computer scientists, and digital-health experts, this collection aims to establish a comprehensive reference for the current and future role of large language models and generative AI in nephrology. Rather than focusing solely on technological advances, the collection emphasizes clinically validated, patient-centered, safe, transparent, and implementable AI solutions. We anticipate that this Research Topic will stimulate international collaboration, identify knowledge gaps, define research priorities, and accelerate responsible adoption of AI technologies that ultimately improve patient outcomes and healthcare delivery worldwide.This Research Topic aims to provide a comprehensive and authoritative overview of the rapidly evolving role of large language models and generative AI across the entire spectrum of nephrology. We seek contributions that bridge technological innovation with clinically meaningful applications while critically evaluating benefits, limitations, safety, ethics, and future directions.We welcome Original Research, Systematic Reviews, Meta-analyses, Narrative Reviews, Clinical Trials, Implementation Studies, Brief Research Reports, Perspectives, Opinion articles, and Technology Reports.Topics of interest include, but are not limited to:• Large language models for clinical decision support in nephrology• Generative AI for chronic kidney disease, acute kidney injury, dialysis, and kidney transplantation• Clinical validation of ChatGPT and other LLMs• AI-assisted diagnosis, prognosis, and individualized risk prediction• Precision nephrology and personalized medicine• Hemodialysis and peritoneal dialysis optimization using AI• Kidney transplant decision support and post-transplant management• Integration of electronic health records, laboratory, pathology, imaging, genomics, and wearable-device data• AI-assisted interpretation of clinical guidelines and evidence synthesis• Clinical documentation, discharge summaries, and workflow automation• Patient communication, education, and shared decision-making• Medical education and simulation using generative AI• AI in clinical research, trial design, data analysis, and scientific writing• Explainable AI, fairness, bias mitigation, privacy, cybersecurity, and governance• Ethical, legal, and regulatory aspects of AI implementation• Real-world implementation studies and health-system integration• Future perspectives for AI-enabled kidney care Topic editors: Ali Veysel Kara, Haofeng Zheng, FNU NEHA, Shahab Saquib Sohail Source: https://www.frontiersin.org/research-topics/84351/large-language-models-and-generative-ai-in-nephrology-from-clinical-decision-support-to-real-world-practice