AI-driven automation for CI/CD pipelines using attention-enabled reptile meta learning

Healthcare systems increasingly rely on software to support critical operations such as patient monitoring, diagnostic decision-making, and real-time data management, making reliability, speed, and continuous availability essential. The software development and deployment phases, therefore, play a crucial role in ensuring that the healthcare applications remain stable and compliant with regulatory standards. However, traditional Continuous Integration/Continuous Deployment (CI/CD) pipelines are largely reactive, relying on manual testing, delayed feedback, and post-failure recovery mechanisms, which are time-consuming and prone to errors. Such limitations lead to deployment instability, inefficient resource utilization, and increased operational risk in mission-critical healthcare environments. To overcome these challenges, automated and intelligent approaches are required to enable proactive failure detection, faster recovery, and reliable deployment. In this regard, a novel AI-driven self-adaptive framework, Continuous Integration/Continuous Deployment Attention-enabled Reptile Meta-Learning (CICD-ARML), is proposed. The proposed system continuously learns from pipeline telemetry and adapts to dynamic deployment conditions, reducing dependency on manual intervention. Experimental results demonstrate that CICD-ARML achieves a 96.8% build success rate, reduces mean recovery time by 35%, reduces deployment risk by 59%, and achieves 94% prediction accuracy. These findings highlight the effectiveness of the proposed framework in enhancing the reliability, efficiency, and adaptability of healthcare software deployment systems.