Leveraging Artificial Intelligence and Machine Learning for Enhancing Decision Support in the Management of Focal Segmental Glomerulosclerosis in Nephrology

Abstract

Objective: FSGS is the primary cause of nephrotic syndrome 
and can progress to end-stage kidney failure. The use of 
Artificial Intelligence and Machine Learning tools enhances the 
comprehensive evaluation of FSGS and aids in treatment 
management to facilitate more informed medical decisions and 
better patient outcomes.
Methods: The investigation examined the utilization of AI/ML 
systems within nephrology research. The study encompassed 
AI/ML research in nephrology sourced from a combination of 
PubMed, IEEE Xplore, and Scopus from 2015 to 2024.
Results: By leveraging AI and machine learning algorithms, the 
precision of FSGS diagnoses is improved, leading to better 
automated predictions about prognosis and more personalized 
treatment plans. Machine learning models yield more accurate 
diagnostic outcomes when compared to conventional 
techniques by examining patient information in conjunction 
with genetic indicators and imaging results.
Conclusion: The management of FSGS will be revolutionized 
by AI and ML technologies that enable swift and precise 
evaluations, enhance treatment strategies, and forecast 
possible results. To ensure the successful integration of these 
FSGS management solutions into clinical practice, it is essential 
to tackle issues related to data reliability and improve the 
transparency of models while incorporating them into clinical 
workflows.