AI-Powered Clinical Decision Systems: Enhancing Diagnostics through Secure Interoperable Data Platforms

Abstract

The rapid growth of healthcare data, driven by electronic health records (EHRs), wearable devices, and diagnostic imaging, has created both opportunities and challenges for clinical decision-making. Traditional Clinical Decision Support Systems (CDSS) often rely on rule-based logic, constrained by limited interoperability and static data structures. In contrast, Artificial Intelligence (AI)-powered CDSS leverage machine learning, deep learning, and natural language processing to interpret complex multimodal datasets, enabling real-time and context-aware diagnostic recommendations. However, the full potential of AI in healthcare is hindered by fragmented data silos, security concerns, and a lack of standardized interoperability frameworks. This paper proposes a secure and interoperable AI-driven clinical decision architecture that integrates federated learning, FHIR-based data exchange, and blockchain-enabled audit trails. The system enables distributed model training without exposing sensitive patient data, ensuring both diagnostic accuracy and compliance with privacy regulations such as HIPAA, GDPR, and India’s DPDP Act. Empirical studies demonstrate that such platforms can improve diagnostic accuracy by up to 25%, reduce clinical decision latency by 40%, and enhance clinician confidence in AI-assisted outcomes. Through a comparative evaluation of existing and emerging CDSS architectures, this research highlights how secure interoperability and AI integration can transform diagnostic pathways, promoting patient safety, scalability, and trust in next-generation healthcare systems.