Improving Delivery Speed and Model Accuracy in Healthcare AI Systems through DevOps Enabled Feedback Mechanisms
Keywords:
Healthcare AI, DevOps, Feedback Loops, CI/CD, Model Accuracy, Delivery Speed, AI Monitoring, Automation\, CNN-LSTM, Clinical Decision Support, Agile DeploymentAbstract
Purpose
This study aims to investigate how integrating DevOps practices into healthcare Artificial Intelligence (AI) systems can enhance clinical decision-making by improving model accuracy and deployment speed. It specifically addresses persistent limitations in translational efficiency between AI model development and real-world clinical deployment.
Design/methodology/approach
A DevOps-enabled feedback mechanism is proposed, embedding real-time performance and outcome feedback loops within continuous integration and continuous deployment (CI/CD) pipelines. The study employs a hybrid experimental design combining controlled system simulations with validation on real-world healthcare datasets to assess the impact of the proposed approach on model precision, deployment frequency, and system responsiveness.
Findings
The results demonstrate that incorporating DevOps-driven feedback loops leads to measurable improvements in AI model accuracy and significantly reduces deployment latency. Continuous monitoring and iterative updates enable rapid correction of performance degradation, resulting in more reliable and adaptive AI systems in clinical environments.
Practical implications
The proposed framework provides healthcare organizations with a practical pathway to operationalize AI systems more effectively. By aligning AI development with DevOps practices, clinical institutions can achieve faster model updates, improved diagnostic reliability, and greater trust in AI-assisted decision-making without disrupting existing workflows.
Originality/value
This study contributes novel insights by bridging DevOps engineering principles with healthcare AI deployment, an area that has received limited empirical attention. The proposed feedback-driven CI/CD framework offers a scalable and reproducible approach to closing the gap between AI research and clinical practice, enhancing both agility and accuracy in AI-driven healthcare services.
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Copyright (c) 2026 Amit Rafique, Asif Rabiul (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
