Meta-analysis: The Role of AI and Machine Learning in the Management of Hemodialysis Patient Data

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) technologies transform clinical decision processes in hemodialysis care. The research evaluates AI/ML models through a systematic assessment of their ability to forecast vital clinical outcomes and optimize dialysis treatment. The research team conducted database searches across Google Scholar, PubMed, IEEE Xplore, and Scopus for studies about AI applications in hemodialysis from 2014 through 2024. The research included peer-reviewed clinical studies that presented clear methodologies and performance metrics. The researchers selected 150 studies for inclusion following their full-text evaluation process. The QUADAS-2 tool evaluated study bias while the random-effects model performed the meta-analysis. AI/ML models achieved remarkable accuracy when forecasting mortality (AUC 0.92), hospitalization (accuracy 89%), and intradialytic hypotension (F1-score 0.81). Deep learning and reinforcement learning models achieved significant improvements in dialysis adequacy and access monitoring. The studies revealed data quality problems in 30% of cases while 65% of clinicians expressed doubts about model interpretability. AI/ML technologies demonstrate significant potential to enhance hemodialysis management through predictive modeling and therapy optimization. The successful clinical adoption of these technologies depends on resolving data quality problems and improving transparency and ethical standards.