A SYSTEMATIC LITERATURE REVIEW ON THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN ENHANCING CARE FOR KIDNEY DISEASES PATIENTS - RAHMAN, MD SAIDUR; MD NOR, NOR SAADAH (Journal of Information Systems and Digital Technologies, 2024)
Chronic Kidney Disease (CKD) is a very long-term condition whereby the kidneys, over time, progressively lose some of their core functionality, resulting in waste accumulation within the body. It may progress silently, with manifestations in advanced stages, and can result in kidney failure that may necessitate dialysis or transplantation. It requires early detection for management. Artificial Intelligence (AI) has emerged as a transformative tool in the management of CKD, enabling more precise diagnosis, treatment optimization, and long-term care. The other way round, AI is a limb of Computer Science dedicated to developing systems that do what a human does intelligently. AI in healthcare has opened several breakthroughs in health care, especially in the management of CKD. Recent development related to AI, including machine learning, natural language processing, and predictive analytics, has gradually integrated all the stages in CKD care, from early diagnosis to treatment optimization, considering the significantly improved diagnostic accuracy and better patient outcomes. AI algorithms use huge datasets ranging from biomarkers to medical imaging in the early diagnosis of kidney dysfunction and provide timely interventions, facilities, and initiation of tailored treatment plans that improve patient outcomes and reduce healthcare costs. AI-based systems enhance decision support for clinicians, improving the management of dialysis and post-transplant care by predicting complications and providing real-time insights. Even with the possible advantages accruing from them, data fragmentation, quality issues, and ethical concerns over patient privacy and decision-making processes continue to be a problem. This review highlights the ongoing challenges related to AI model generalizability across diverse patient populations and the need for more transparent and standardized validation processes. Strong data management is required in this, with due adherence to ethical guidelines, so that AI makes its way into kidney care in a way that's fair and secure. Addressing these challenges requires cross-disciplinary collaboration between AI researchers, nephrologists, and policymakers to ensure safe and equitable integration of AI in clinical practice. The objective of the research is to systematically evaluate AI technology in CKD from a patient-centered perspective of care improvement for patients with CKD. Fourteen studies published from 2018 to 2024 were reviewed in the systematic review to learn how AI technology was incorporated to improve care for CKD patients. These studies demonstrate the increasing role of AI in identifying biomarkers, predicting disease progression, and personalizing treatment protocols. In addition, most of the research those were reviewed was published in Science Directory, IEEE Xplore and Emerald Insights database and done by university students for improvement in the care of CKD patients using AI. The findings from this review suggest that while progress has been made, there is still a need for more rigorous validation and real-world evidence to fully realize the potential of AI in CKD management.