Advanced Kidney Failure Identification Using Robotic Process Automation with Augmented Intelligence and IoT-Based an Integrated Healthcare System

Chronic Kidney Failure (CKF) remains a significant health concern worldwide, necessitating advanced diagnostic techniques for timely intervention. This research delves into the integration of Robotic Process Automation (RPA) with Augmented Intelligence and the Internet of Things (loT) for enhanced C...

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Bibliographic Details
Published in:Journal of Electrical Systems 2024-04, Vol.20 (2s), p.846-856
Main Authors: Lalitha, S, Vinutha, K, Rama, R Senthil, Prabhu, B, Rufus, N Herald Anantha, Srikanth, R
Format: Article
Language:English
Subjects:
Automation
Diagnostic systems
Intelligence
Internet of Things
Kidney diseases
Kidneys
Medical research
Real time
Online Access:Request full text
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Summary:Chronic Kidney Failure (CKF) remains a significant health concern worldwide, necessitating advanced diagnostic techniques for timely intervention. This research delves into the integration of Robotic Process Automation (RPA) with Augmented Intelligence and the Internet of Things (loT) for enhanced CKF detection. The proposed RPA system showcased a diagnostic accuracy of 92%, a notable improvement from the 75% observed with traditional methods. Moreover, the system efficiently delineated the kidney contour in an average of 20 seconds, considerably faster than existing techniques. The collaborative force of Augmented Intelligence and loT was instrumental in achieving these results, emphasizing real-time data collection coupled with sophisticated analysis. This fusion not only bolstered accuracy but also emphasized early detection, with the system's capability to provide instant notifications enhancing the potential for proactive interventions. In essence, this research underscores the transformative potential of integrating technological advancements with medical expertise, offering a promising avenue for CKF diagnosis and potentially reshaping the landscape of medical diagnostics in other domains.
ISSN:1112-5209
DOI:10.52783/jes.1680