# Interoperability Reference Models for Applications of Artificial Intelligence in Medical Imaging
## 4 Discussion  
The application range of data including medical imaging is quickly expanding, and the need for data collected and used in limited locations is now being demanded in multiple different locations. As such, the need to establish an environment that enables the exchange and use of medical imaging among different institutions is becoming more important and is the main goal of establishing interoperability. In approaching medical imaging from the concept of interoperability, this study allowed for the exchange of medical data between two systems through an HL7 FHIR server and designated the exchange format and structure through resources and XML according to HL7 FHIR standards. Moreover, for the safe exchange of data, it used a CTP module using de-identifier methods by HIPAA and DICOM in order to minimize personal information related issues that could occur systematically. It also designed a process that minimized measurement uncertainty through the use of phantoms in order to display meaningful values regardless of the institution in which the medical imaging data derived from and simultaneously confirmed the de-identifier and medical data standards currently being used. Figure 14: Medical Imaging Interoperability Reference Model. In the case of medical imaging, as different measurement standards are used for each hospital and medical equipment, it is normal for a medical professional to perform a qualitative review. Uncertainty minimization methods using standardized phantoms allow medical imaging to be displayed in a qualitative manner and also allows for the use of quantitative medical imaging between different institutions when the same standards are used. Pre-existing medical data standards such as HL7 FHIR can be used for multiple medical institutions to use the same medical data standards and guarantee compatibility between systems. Moreover, by using non-identifier methods such as HIPAA and DICOM for the protection of personal information in medical data, legal issues can be minimized as well. When applying the interoperability features as defined by HIMSS, the first basic feature is defined as such that medical data should be able to be shared between two systems. We confirmed that medical imaging related data such as imaging study, patient, endpoint, and phantom data can be transferred through a medical data exchange server based on HL7 standards. The second feature as defined by HIMSS states that formats and structures must be defined so that data cannot be tampered with or modified. The data formats within the HL7 FHIR server are defined as XML and JSON with the data structure following Resource. The third feature states that no issues should arise in the safe exchange, use and interpretation of medical information between two or more medical systems. By using phantom data, medical images collected from different locations can be used in a qualitative manner by applying the same standards. The interoperability reference model created can enable the use of medical imaging on a large scale. As a large amount of data is required for deep learning, the process of increasing the quality of data before image training is important. It is necessary to check whether quantified images increase deep learning performance; we will conduct a further study that shows the results of applying deep learning techniques to MRI images with a reference model. The use of medical devices that apply artificial intelligence is growing within the medical devices industry. For example, integrating the reference model to other medical visualization modules that can transfer informative medical volume such as clustering data [25] and are suitable for handing large amounts of medical imaging data [26] is able to contribute to improving medical image artificial intelligence performance. In addition, the U.S. Food and Drug Administration (FDA) has defined the concept of Software as a Medical Device (SaMD) and allowed the foundation for artificial intelligence to be certified as a medical tool [27].