Advances in AI-based combination therapies versus standard treatments for heart failure in patients with advanced kidney injury

Background: Recent advancements in AI and Machine Learning (ML) can help manage kidney disease and heart failure by providing predictive analytics and personalized treatment plans, improving diagnostic accuracy, and assisting clinicians in controlling chronic disease symptoms like acute kidney injury (AKI) and Managing Heart Failure (HF). We evaluated AI and ML role in predicting AKI and managing HF patients and it involved analyzing various predictive models and technologies to improve early detection provide personalized treatment, and enhance overall patient outcomes in complex renal and cardiac conditions. Methodology: We conducted our search on databases Scopus, PubMed, and Web of Science. Empirical evidence and state of the art of cutting-edge literature and real-time field research was analyzed. Results: AI advancements in HF management for advanced kidney disease AKI include unsupervised machine learning for risk stratification, reinforcement learning for predictive modeling and dynamic management, and LLMs and chatbots for diagnostic support and patient education. New developments in AI and medical technology are predictive analytics for AKI, automated ultrasound interpretation, AI-enhanced dialysis monitoring, HF risk prediction, remote monitoring, and telehealth integration, and making personal treatment strategies according to patient needs. Innovations like wearable dialysis devices, bioengineered kidney tissues, ECMO, LVADs, TAVR, SGLT2 inhibitors, CRT, and nephroprotective drugs have also improved patient outcomes as these have enabled personalized care and early intervention strategies. Conclusion: It is possible to conclude that AI has revolutionized AKI and HF management. Novel technologies offer precise and adaptive care that strictly addresses individual patient needs.