AIQNET: Project Archive & Overview
AIQNET Project Overview
AIQNET was the result of an initiative to build a Medical Data Ecosystem of a consortium that won the German Federal Government’s AI Competition (KI-Wettbewerb) in 2019 under the project name KIKS.
The curated network was comprised of 16 funded project partners and a growing number of associated partners, bringing together internationally recognized established leaders in the medical technology industry and the delivery of healthcare. RAYLYTIC was the leader of the consortium.
Mission & Vision
The goal of the AIQNET project was to address a fundamental challenge in modern healthcare: the fragmentation of medical data across different ecosystems, organizations, and technologies.
By uniting a selective consortium of industry leaders, AIQNET aimed to create a secure, legally compliant Medical Data Ecosystem to facilitate evidence-based healthcare delivery through standardized data sharing practices and AI-powered analysis.
Consortium Strength
The ecosystem’s competitive advantage was its carefully curated membership. With 16 funded core partners representing internationally recognized organizations across medical device manufacturing, hospital systems, and healthcare IT, AIQNET established itself as an authoritative voice in healthcare data interoperability. This structure enabled rapid advancement of shared standards while maintaining a strategic focus on practical implementation and market relevance.
Key Achievements
Interoperable Infrastructure: AIQNET successfully developed a cross-sectoral framework that unified leading healthcare providers, medical device manufacturers, hospitals, and software developers into a cohesive data ecosystem, enabling seamless data exchange regardless of underlying systems or organizational boundaries.
Regulatory Compliance Framework: The platform established comprehensive legal and ethical safeguards, including patient consent management, purpose-bound data usage protocols, IT security standards, and advanced pseudonymization and anonymization measures that ensured full compliance with healthcare regulations.
Data Standardization: AIQNET implemented universal data format translation (FHIR standard), converting unstructured data such as imaging files and laboratory reports into analyzable, shareable formats that could be provisioned in anonymized or pseudonymized forms as required.
AI-Driven Analytics: The platform provided specialized analytical applications that leveraged healthcare data for diagnostic and therapeutic optimization, as well as automation of administrative processes, demonstrating the practical value of standardized data access.
Growing Ecosystem: Beyond the 16 core funded partners, AIQNET attracted an expanding network of associated partners, extending its influence and creating a thriving ecosystem of organizations invested in healthcare data interoperability.
The Next Generation of Data Exchange Between Software Systems: SMART on FHIR
This project focused on developing the next generation of data exchange between software systems based on the SMART on FHIR specification. While this approach had long been established as a standard in the U.S. healthcare sector, it was still in its early stages in Europe. The project aimed to create a consistent framework for security, privacy, and data model requirements within the European healthcare landscape.
In practice, the fragmentation of healthcare information posed a major challenge to implementing effective strategies. AIQNET addressed this issue by providing a SMART on FHIR specification that standardized access to and use of medical data.
Integration Server
The use of SMART on FHIR was enabled on existing infrastructure. Integration was achieved through an integration server capable of handling multiple standards and protocols, including HL7 v2, v3, CDA, FHIR, and DICOM. This made it possible to connect diverse systems and exchange data efficiently.
Decentralized Architecture
The system was built on a decentralized architecture, ensuring that patient-identifiable data always remained within a protected environment controlled by the data owner. This approach guaranteed a high level of data sovereignty and security.
Data Protection and Compliance
Through a data governance framework managed by hospitals, data could be securely and lawfully shared with external parties when necessary. This concept ensured full compliance with data protection regulations while maintaining control over sensitive information.
One System, One Data Model for Greater Interoperability
AIQNET processed both structured and unstructured information from a variety of medical systems—such as HIS, LIS, RIS, and PACS—and made it available in a unified system with a common data model. Unstructured texts, wearable data, PDF files, and radiological images were processed by third-party applications that extracted relevant information, including medical context, and converted it into structured data.
Benefits for Developers and Hospitals
The project also reduced barriers for software providers, enabling them to integrate their own solutions into hospitals without having to overcome complex technical challenges. At the same time, it significantly reduced the integration workload within hospital IT departments, leading to greater efficiency and faster deployment of innovative healthcare applications.
Practical Applications & Use Cases
The AIQNET ecosystem demonstrated concrete value through specialized applications developed by consortium partners:
Inomed: AI-enabled collection, structuring, and processing of clinical data for technical documentation of medical device manufacturers, streamlining regulatory compliance and reducing time-to-market for new devices.
TZM GmbH: Universal data exchange between medical devices and systems, solving a critical interoperability challenge that had previously required custom integrations and complex workarounds.
Aesculap AG: Digital Post Market Surveillance (PMS) and Post-Market Clinical Follow-Up (PMCF) studies, enabling manufacturers to efficiently conduct post-release monitoring and clinical validation studies through standardized data access.
These use cases exemplified how the AIQNET platform translated interoperability principles into tangible business value across the healthcare technology value chain.
Resources
For more detailed information about AIQNET, please refer to the project documentation:
