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Modernizing Healthcare Software Delivery through Predictive AI Decision Support Cybersecurity and Real-Time Threat Detection

Dr.R.Sugumar

Professor, Department of Computer Science & Engineering, SIMATS Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, India

Abstract

Modern healthcare software systems demand rapid feature delivery while maintaining stringent requirements for security, reliability, regulatory compliance, and patient safety. Conventional software delivery practices often struggle to detect emerging cyber threats, respond to evolving vulnerabilities, and support intelligent decision-making throughout the software development lifecycle. This paper presents an AI-driven framework that modernizes healthcare software delivery by integrating predictive artificial intelligence, decision support, cybersecurity, and real-time threat detection into a unified software engineering ecosystem. The proposed framework leverages machine learning algorithms to continuously analyze source code repositories, software logs, infrastructure telemetry, vulnerability databases, and operational metrics to predict software defects, identify security risks, prioritize remediation strategies, and support proactive engineering decisions. An intelligent cybersecurity layer incorporates real-time threat detection through anomaly detection, behavioral analytics, and continuous security monitoring to identify malicious activities, insider threats, ransomware attempts, and unauthorized access before they impact clinical services. Cloud-native deployment, automated security validation, API security monitoring, and adaptive risk assessment further strengthen the resilience of healthcare applications while ensuring regulatory compliance with healthcare security standards. The framework also employs predictive analytics to optimize release planning, improve resource allocation, minimize deployment failures, and enhance system availability across distributed healthcare environments. Experimental evaluation demonstrates improvements in software delivery efficiency, prediction accuracy, vulnerability detection rates, incident response time, and overall system reliability compared with conventional software delivery approaches. The proposed architecture enables healthcare organizations to transition from reactive software maintenance to intelligent, proactive, and secure software delivery, supporting resilient digital healthcare ecosystems capable of sustaining continuous innovation while protecting sensitive patient information and critical healthcare infrastructure. This research contributes a scalable and practical framework for integrating predictive AI, intelligent decision support, cybersecurity, and real-time threat detection into modern healthcare software engineering practices

Article Information

Journal

International Journal of Emerging Trends in Engineering and Management Research

Volume (Issue)

Vol. 11 No. 2 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)

DOI

https://doi.org/10.15662/ijetemr.2026.1102005

Pages

19643-19651

Published

April 18, 2026
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Dr.R.Sugumar (%2026). Modernizing Healthcare Software Delivery through Predictive AI Decision Support Cybersecurity and Real-Time Threat Detection. International Journal of Emerging Trends in Engineering and Management Research , Vol. 11 No. 2 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 19643-19651. https://doi.org/10.15662/ijetemr.2026.1102005

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