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ARTIFICIAL INTELLIGENCE IN HEALTHCARE: ADVANCEMENTS IN KIDNEY DISEASE DIAGNOSIS AND MANAGEMENT

Raghu Ram Chowdary Velevela

Abstract


Kidney diseases, particularly Chronic Kidney Disease (CKD), represent a significant and growing global health burden. CKD often progresses silently, with patients exhibiting few to no symptoms during the early stages, making timely diagnosis and intervention a critical challenge. The rising incidence of CKD, driven by factors such as diabetes, hypertension, and aging populations, necessitates the development of innovative strategies for early detection, accurate diagnosis, and effective management. Recent advancements in Artificial Intelligence (AI) have introduced transformative capabilities within the field of nephrology. AI technologies—including machine learning, deep learning, and natural language processing—are now being leveraged to enhance various aspects of renal care. These include the prediction of CKD onset and progression, automated estimation of glomerular filtration rate (GFR), image-based diagnosis through radiological and histopathological data analysis, and patient stratification for personalized treatment planning.AI-driven systems have demonstrated remarkable accuracy in identifying at-risk populations, monitoring disease progression, and facilitating clinical decision-making. Furthermore, these systems contribute to reducing human error, improving diagnostic consistency, and optimizing healthcare resource allocation. However, the integration of AI in nephrology is not without challenges. Issues such as data privacy, algorithmic bias, model interpretability, and the need for large, diverse datasets pose barriers to widespread clinical adoption. This paper provides a comprehensive review of the current state of AI applications in nephrology, examines the technologies and methodologies employed, discusses existing challenges, and explores the future directions for AI-enhanced kidney disease management. As the synergy between AI and nephrology continues to evolve, it holds the potential to revolutionize renal healthcare delivery, enabling more proactive, precise, and patient-centered approaches.

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References


Chan, L., Chaudhary, K., Saha, A., Chauhan, K., Vaid, A., Zhao, S., ... & Coca, S. G. (2019). Predicting acute kidney injury using deep learning algorithms in critically ill patients. Nature Communications, 10(1), 1-10. https://doi.org/10.1038/s41467-019-10569-1

Gulshan, V., Rajan, R. P., Widner, K., Sundararajan, S., Jayadevan, R., Chodpathumwan, Y., ... & Corrado, G. S. (2020). Performance of a deep-learning algorithm vs manual grading for detecting diabetic retinopathy in India. JAMA Ophthalmology, 138(7), 705–712. https://doi.org/10.1001/jamaophthalmol.2020.1301

Koyner, J. L., Carey, K. A., Edelson, D. P., & Churpek, M. M. (2018). The development of a machine learning inpatient acute kidney injury prediction model. Critical Care Medicine, 46(7), 1070-1077. https://doi.org/10.1097/CCM.0000000000003123

Tomašev, N., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Saraiva, A., ... & Suleyman, M. (2019). A clinically applicable approach to continuous prediction of future acute kidney injury. Nature, 572(7767), 116–119. https://doi.org/10.1038/s41586-019-1390-1

Xu, X., Wang, H., Liu, Y., Wang, Y., Wang, M., & Qiu, M. (2021). AI in nephrology: Emerging applications and future directions. Kidney International Reports, 6(10), 2589-2597. https://doi.org/10.1016/j.ekir.2021.07.016

Mohamadlou, H., Lynn-Palevsky, A., Barton, C., Chettipally, U., Shieh, L., Calvert, J., ... & Das, R. (2018). Prediction of acute kidney injury with a machine learning algorithm using electronic health record data. Canadian Journal of Kidney Health and Disease, 5, 2054358118776326. https://doi.org/10.1177/2054358118776326

Suresh, H., & Guttag, J. V. (2021). A framework for understanding unintended consequences of machine learning. Communications of the ACM, 64(11), 62–71. https://doi.org/10.1145/3453326

Sharma, S., Wasson, S., & Srivastava, S. (2022). Role of artificial intelligence in early diagnosis of chronic kidney disease. Biomedical Signal Processing and Control, 71, 103187. https://doi.org/10.1016/j.bspc.2021.103187

Nadkarni, G. N., Coca, S. G., & Gharavi, A. G. (2022). Integrating artificial intelligence in nephrology: Rationale, opportunities, and challenges. Nature Reviews Nephrology, 18, 653–664. https://doi.org/10.1038/s41581-022-00597-1

Ravizza, S., De Momi, E., & Ferrigno, G. (2019). Artificial intelligence in nephrology: A comprehensive review. Artificial Intelligence in Medicine, 96, 12-21. https://doi.org/10.1016/j.artmed.2019.04.005


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