Abstract
The Internet of Medical Things (IoMT) has the potential to change the healthcare industry with the help of sensing technologies. However, the increasing number of heterogeneous devices creates critical data silos due to the use of proprietary data formats, such as Apple Health and Fitbit. These data formats are not compatible with standardized electronic health record (EHR) systems. Fast Healthcare Interoperability Resources (FHIR) and Federated Learning (FL) have the potential to bridge the gap between the incompatibilities of data formats while maintaining the privacy of the patient. This systematic review has included the recent literature from the year 2020 to 2025. New findings on the integration of FL and FHIR have also been included in this systematic review. A novel FedFHIR architecture has been introduced with the help of on-device converters that standardize raw data before FL processing. This helps in overcoming the problems of interoperability, governance, and edge resource constraints. The regional issues faced by people living in rural and Central Asia have also been addressed in this systematic review. Semantic interoperability is a prerequisite for the scaling of privacy-preserving artificial intelligence (AI) in a distributed healthcare system.