Vol. 5 No. 1 (2021): International Research Journal of Innovative Engineering (IRJIE)

Explainable Artificial Intelligence (XAI) has become a crucial advancement in healthcare informatics, particularly in Clinical Decision Support Systems (CDSS) and healthcare recommendation systems. While traditional machine learning and deep learning models provide high predictive accuracy in tasks such as disease diagnosis, risk prediction, and treatment recommendation, they often operate as opaque “black-box” systems. This lack of interpretability limits clinician trust, reduces adoption in real-world healthcare settings, and raises ethical concerns regarding accountability and transparency. XAI addresses these challenges by offering interpretable, transparent, and human-understandable explanations for model predictions. Techniques such as SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), decision trees, and attention-based neural networks are widely used to enhance explainability in clinical systems. These approaches help healthcare professionals understand which features influence predictions and how decisions are generated. The integration of XAI into CDSS improves diagnostic accuracy, supports personalized treatment planning, reduces medical errors, and enhances patient safety. Furthermore, explainable healthcare recommendation systems improve patient engagement and trust by making AI-driven suggestions more transparent. This study explores key XAI techniques, evaluates their role in clinical decision-making, reviews existing literature, and proposes a structured methodology for developing interpretable healthcare AI models. The findings emphasize that combining accuracy with explainability is essential for safe, ethical, and effective deployment of AI in healthcare environments

The rapid adoption of enterprise cloud computing has significantly increased the dependence on Application Programming Interfaces (APIs) for integrating distributed applications, platforms, and business services. As organizations embrace multi-cloud and hybrid cloud environments, managing APIs securely and efficiently has become increasingly complex due to evolving cyber threats, dynamic workloads, and heterogeneous infrastructures. Intelligent API orchestration powered by Artificial Intelligence (AI)-enabled decision intelligence offers an innovative approach to addressing these challenges by automating service coordination, optimizing resource allocation, and strengthening security mechanisms. This study examines how AI-driven decision intelligence enhances API orchestration through predictive analytics, anomaly detection, policy automation, and real-time decision-making. The proposed approach integrates machine learning, cloud security frameworks, and intelligent orchestration engines to improve service reliability, minimize latency, and ensure regulatory compliance. Furthermore, AI models continuously analyze operational data to identify vulnerabilities, predict failures, and recommend optimal orchestration strategies. The research adopts a qualitative analytical methodology based on an extensive review of existing literature and industry practices to evaluate the effectiveness of AI-enabled orchestration in enterprise cloud ecosystems. The findings indicate that intelligent API orchestration improves operational efficiency, enhances cybersecurity resilience, supports adaptive governance, and enables scalable cloud service management. Consequently, AI-enabled decision intelligence represents a transformative solution for achieving secure, autonomous, and resilient enterprise cloud services in modern digital environments