Metrology in explainable image-based medical diagnostics, such as pathology or radiology, often relies on the subjective expertise of experienced medical professionals. However, this process can be greatly enhanced, supported, or even supplanted by automated, AI-driven digital processing pipelines. For diagnostics to be secure, trustworthy, explainable, and reliable through non-human-centric methods, several key requirements must be met. To achieve accurate, explainable, and dependable diagnostics in metrology-compliant image-based digital medicine, diagnosticians must work within an approved framework governed by defined standards, such as the In-vitro Diagnostic Device Regulation (IVDR), Medical Device Regulation (MDR) and the AI Act. This paper reviews challenges posed by new regulations on AI in medical diagnostics and explores how an international data spaces (IDS) approach can be applied to validate AI methods. Specifically, this report addresses the challenge of outlining processes that enable successful legal and methodological validation through the exchange of data and certificates. The primary goal of the study is to demonstrate the potential of Gaia-X-based solutions for managing and operating a federated ecosystem. A key research objective is to design a viable implementation of such a W3C-compliant ecosystem.