Interoperability Challenges and Solutions in AI-Driven Healthcare Systems

Artificial intelligence (AI) is increasingly being applied in healthcare to support individualized treatment planning and enhance diagnostic accuracy, offering significant potential to transform clinical practice and patient care. As AI technologies advance rapidly, it becomes essential to ensure their development is ethical, equitable, and aligned with patient needs. However, achieving interoperability within AI-driven healthcare systems remains a major challenge, particularly in areas such as data standardization, system integration, and regulatory compliance. Addressing these challenges is crucial for the full realization of AI&s;s benefits. This work discusses key interoperability barriers and outlines potential solutions, including the adoption of universal data standards, the creation of interoperable AI platforms, and the strengthening of regulatory frameworks to support seamless AI integration. Additionally, case studies of successful AI implementations in healthcare are examined to illustrate best practices and innovative strategies that can guide the development of an interoperable and effective AI-enabled healthcare ecosystem.