Strategic Framework for Integrating AI-Based Teleradiology Solutions into Modern Healthcare Ecosystem for Predictable Response Time, Enhanced Connectivity, Patient Empowerment, Flexibility, and Innovation

Abstract

The integration of Artificial Intelligence (AI)-based teleradiology solutions into modern healthcare systems presents a transformative opportunity to enhance diagnostic speed, connectivity, patient empowerment, flexibility, and innovation in healthcare services. This thesis develops a strategic framework that guides service providers in deploying AI-driven teleradiology solutions to ensure predictable response times and support real-time collaboration among radiologists, physicians, and patients. By analyzing current literature, conducting various field studies, and examining successful implementations, this research identifies key considerations, challenges, and best practices for successful AI integration. The framework emphasizes technical interoperability, data security, patient-centric design, and adaptive workflows, which together form a robust ecosystem supporting swift and accurate diagnosis and treatment. Insights from this study aim to help healthcare organizations align AI innovations with their strategic goals, ultimately fostering a patient-centric model that leverages technological advances to deliver more responsive, connected, and flexible radiology services.