Why Translate Clinical Innovation?
Between a validated idea and clinical reality lies a critical step.
Many data-driven and AI-based approaches remain confined to research settings or pilot projects. The transition into routine clinical care often fails due to insufficient validation, lack of integration into existing processes, or unclear relevance for patients.
The path from an idea to clinical application comprises many steps of the innovation chain: data capture, integration, data quality, annotation, model development, clinical validation, regulatory assessment, implementation and outcome evaluation. AI²POC maps this chain in a shared infrastructure.
Technology alone is not sufficient. Every application must be scientifically validated, regulatorily assessed, clinically implementable, economically viable and aligned with benefit for patients.
AI²POC therefore treats translation not as a downstream technology transfer, but as a research focus in its own right: clinical expertise, data science, regulatory requirements and care processes are connected so that innovations can deliver benefit in real perioperative workflows.
What do we want to understand?
Clinical Need & Innovation Chain
Which perioperative problems can be meaningfully addressed through digital or data-driven approaches — and which steps from data capture to outcome evaluation are required?
Validation & Evidence
How can new methods be scientifically tested, evaluated in clinically relevant settings, and at the same time meet regulatory and interoperability requirements?
Data Platform & Implementation
What conditions are required to integrate multimodal data sources — AIMS, PDMS, high-frequency signals, clinical and patient-generated data — into real care processes?
Benefit & Outcomes
What measurable value do explainable, traceable innovations create for care delivery, clinical teams, and patient-centered outcomes?