Interoperability Reference Models for Applications of Artificial Intelligence in Medical Imaging

Abstract

Medical imaging is currently being applied in artificial intelligence and big data technologies in data formats. In order for medical imaging collected from different institutions and systems to be used for artificial intelligence data, interoperability is becoming a key element. Whilst interoperability is currently guaranteed through medical data standards, compliance to personal information protection laws, and other methods, a standard solution for measurement values is deemed to be necessary in order for further applications as artificial intelligence data. As a result, this study proposes a model for interoperability in medical data standards, personal information protection methods, and medical imaging measurements. This model applies Health Level Seven (HL7) and Digital Imaging and Communications in Medicine (DICOM) standards to medical imaging data standards and enables increased accessibility towards medical imaging data in the compliance of personal information protection laws through the use of de-identifying methods. This study focuses on offering a standard for the measurement values of standard materials that addresses uncertainty in measurements that pre-existing medical imaging measurement standards did not provide. The study finds that medical imaging data standards conform to pre-existing standards and also provide protection to personal information within any medical images through de-identifying methods. Moreover, it proposes a reference model that increases interoperability by composing a process that minimizes uncertainty using standard materials. The interoperability reference model is expected to assist artificial intelligence systems using medical imaging and further enhance the resilience of future health technologies and system development.

Empowering PET: harnessing deep learning for improved clinical insight

“…However, inadequate data interoperability can have a significant impact on the accuracy of AI predictions and a reference model should be embraced to establish robust data. Approaches to minimise measurement uncertainty, enable data identification, and standardise pre-existing data for research purposes are of paramount importance for data integration and enhanced AI applications in nuclear medicine [ 72 ]. Several open-source and proprietary software tools have been developed by exploiting the availability of large amounts of data for learning (open libraries, etc.).…”

Reviewing the Impact of Embedded Systems in Medical Devices in the Usa

“…The interconnection of medical devices is emerging as a new requirement in modern medicine, and various communication standards have been proposed to achieve this (Venkatasubramanian et al, 2014). The interoperability reference model proposed by is believed to provide further developments to the artificial intelligence medical devices industry (Kwon & Yoo, 2021). In conclusion, the integration of AI in embedded systems, data analytics for real-time patient monitoring, and connectivity and interoperability among different devices have significantly advanced the impact of embedded systems in medical devices in the USA.…”

Blockchain Interoperability On Healthcare: A Systematic Review

“…Reference Healthcare issues that need to be addressed (Kwon & Yoo, 2021;Kothari et al, 2021;Usman et al, 2021;Vervoort et al, 2020) Lack of research in the healthcare domain based on blockchain interoperability, interoperability implementation of different technologies (Hasselgren et al, 2020;Kothari et al, 2021;Tandon et al, 2020) Data security, Data integrity (Sharma & Balamurugan, 2020) User authentication (Tariq et al, Furthermore, they identified user authentication, blockchains, and IoT in the drug supply chain, data exchange structure, performance, cost, efficiency, and big data of medical records, projects that are not integrated, privacy and scalability as issues that need to be addressed.…”

“…Interoperability is defined as the capability of different systems to communicate with each other without limitations (Kwon & Yoo, 2021). By exchanging data between multiple systems and allowing access to it simultaneously, blockchain technology can improve interoperability.…”

“…The medical field defines interoperability as a means of optimizing the health of individuals and society through the compatibility of different medical information systems, medical equipment, and applications (Kwon & Yoo, 2021).…”