# Interoperability Reference Models for Applications of Artificial Intelligence in Medical Imaging
## 1 Introduction  
A key component of the Fourth Industrial Revolution is the convergence of information and communication technology (ICT). Artificial intelligence and big data comprise the ICT field and is highly expected to be future growth industries. As such, the scope in which data are used is rapidly changing. In the past, research using data was applied in controlled environments in which resources could be collected. The range of artificial intelligence and big data has expanded the data application to the medical imaging domain [1, 2]. Therefore, artificial intelligence and big data are applied to medical imaging.  
As the range in which data could be applied expanded from a single institution to multiple institutions, a uniform data standard was important, and data collectors faced challenges in controlling the entire process by which data were collected and applied [3]. Medical imaging standards such as Health Level Seven (HL7), Digital Imaging and Communications in Medicine (DICOM), and other standards were implemented with multiple institutions using the same standards [4]. There is also research being conducted in developing de-identifying methods in order to protect any personal information present in medical imaging collected from environments in which collectors are unable to monitor the entire data collection process. De-identifying methods process any personal informationthat could potentially be used to specify an individual and retain any data necessary for research. In the case of de-identifying methods, as any personal identifiers are removed, it does not conflict with personal information protection laws. These methods minimize the risk of personal information leaks and maximize accessibility towards data [5].  
Multiple medical institutions have their medical specialists review medical imaging that have different measurement values due to differing medical equipment. As interest in artificial intelligence capable of deciphering medical imaging increases, there is a need for quantifying medical imaging for more precise diagnoses [6]. Currently, a large amount of artificial research in the medical imaging field is being conducted with magnetic resonance imaging (MRI), which is used to identify and predict diseases and to provide forecasts after prognosis, among others [7]. In the case of MRI, different environments at medical institutions and the conditions of the patient may affect the magnetic field, resulting in vastly different measurement values even with the same subject. Such results are defined as measurement uncertainty, and other medical imaging measurement equipment have similar occurrences of measurement uncertainty. If measurement uncertainty can be minimized, medical imaging data can have a meaningful value regardless of which institution collected it. To minimize uncertainty, the measurement value of phantoms, which are standard materials, can be used as standardized values in a process that supplements actual measured values.  
In order to establish the interoperability of medical imaging and allow for medical imaging to be used at different institutions, this study aimed to propose an interoperability reference model that uses methods which minimize measurement uncertainty, enable de-identifiers, and standardize pre-existing data used for research.  
The contributions of this study can be summarized as follows:  
1. It proposes an approach to increase interoperability of medical imaging measurements from different medical institutions by using methods reference phantoms.
2. It addresses the systematic compatibility issue by applying pre-existing medical data specification standards in HL7 and personal information protection methods in the Health Insurance Portability and Accountability Act (HIPAA) and DICOM.
3. It follows the characteristics of interoperability as defined by the Healthcare Information and Management Systems Society, Inc. (HIMSS), and applied in medical imaging.