Background Artificial intelligence (AI) increasingly impacts medicine and medical specialties, including nephrology. Technologies such as large language models (LLMs), decision-support AI, and machine learning-powered predictive analytics enhance clinical care. These AI-driven tools show great potential in areas such as predicting the risk of chronic kidney disease, managing dialysis, supporting kidney transplantation, and treating CKD and diabetes-related kidney issues. Summary General AI platforms like ChatGPT, Bard, and Google Gemini are useful for education and synthesizing knowledge. In contrast, specialized medical AI systems such as KidneyIntelX and DreaMed Advisor provide clinically validated decision support systems that aid physicians in patient care. Retrieval-augmented generation (RAG) enhances LLMs by accessing real-time medical data and research insights, reducing misinformation risks, and ensuring accurate, verified medical responses. However, LLMs still face challenges in adapting to complex patient cases. The effectiveness of RAG depends on the quality of the data retrieved and adherence to ethical and confidentiality standards, with human oversight often necessary. Key Messages • Improving AI accuracy, increasing model transparency, and ensuring seamless integration into clinical settings maximize AI benefits in nephrology. • Regulatory approvals and validation are essential to build trust among patients, physicians, and healthcare institutions. • When integrated correctly into clinical workflows, AI can transform nephrology practice by providing efficient, data-driven insights, improving patient outcomes, and reducing administrative burdens. • Ethical, responsible adoption with stringent oversight is crucial for successfully implementing AI in nephrology.
Zoccali et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: