Improving Disease Detection Accuracy with Al and Secure Data Exchange through API Gateways

Abstract

The rapid proliferation of heterogeneous healthcare data sources—ranging from wearable devices and laboratory systems to advanced medical imaging—has created both opportunities and challenges for disease detection and clinical decision-making. Artificial Intelligence (AI) has demonstrated significant potential in improving diagnostic accuracy and enabling predictive healthcare, but its effectiveness depends heavily on the availability of secure, integrated, and high-quality data. Traditional data-sharing mechanisms are often hindered by silos, interoperability issues, and concerns over data privacy and compliance. To address these challenges, secure Application Programming Interface (API) gateways are emerging as a critical enabler of healthcare data exchange. By enforcing strong encryption, authentication, and authorization protocols, API gateways ensure that sensitive health data flows seamlessly and securely between distributed data sources and AI-driven disease detection engines. This paper explores how secure API gateways enhance disease detection accuracy by enabling smooth data integration, maintaining data integrity, and safeguarding patient privacy. A conceptual architecture and case study are presented to illustrate measurable improvements in detection accuracy when leveraging encrypted, authenticated API-mediated data flows. Furthermore, the paper highlights key challenges, including scalability, latency, and regulatory compliance, and discusses future directions such as privacy-preserving AI and blockchain-enabled APIs. The findings underscore that the convergence of secure API infrastructures and AI-driven analytics is fundamental to achieving reliable, accurate, and compliant disease detection in next-generation healthcare systems.