Danube Private University (DPU) — Living-Lab Validation & Regulatory-Grade Evidence Partner

Danube Private University (DPU, Austria) is seeking to join Horizon Europe consortia as a dedicated clinical living-lab and evidence-generation partner for innovative health technologies. Located in the UNESCO World Heritage region of Wachau, we bridge the often-difficult gap between academic prototyping and real-world clinical utility. Our institution combines the agility of a private university with the substantial clinical infrastructure of three affiliated University Hospitals (Wiener Neustadt, Neunkirchen, Hochegg) and the Dental Clinic Krems.

Many Horizon Health proposals excel in technological innovation but falter in their translation strategies—struggling to define credible intended uses, realistic workflows, or regulatory-grade validation plans. DPU’s strategic value lies in hardening these aspects. We transform technology-led projects into deployable, defensible solutions by anchoring them in real clinical environments and producing evidence packages designed to withstand the scrutiny of reviewers and future regulatory bodies.

What DPU can deliver (Technical Offering)

1) Clinical living-lab validation in real workflows We offer more than theoretical pilots. DPU can host and run comprehensive living-lab activities within its clinical environments to stress-test feasibility and usability early in the project lifecycle. By embedding innovations into actual dental and medical workflows, we support user-needs validation, process mapping, and adherence studies. Our aim is to generate usable evidence regarding what works in practice, identifying failure points early so they can be redesigned without inflating the project into an unrealistic clinical trial.

2) Regulatory-grade evaluation and evidence strategy We design robust, transparent validation plans that coordinators can utilise as the project's "evidence backbone." This involves defining clear claims and intended uses, mapping the context of use, and establishing a risk-based validation strategy that traces requirements to test results. For AI-enabled tools, specifically under HORIZON-HLTH-2026-01-IND-03, we address critical factors such as generalisability, dataset shift, drift monitoring, and "clinician-in-the-loop" evaluation—elements that evaluators increasingly expect but which remain under-addressed in many proposals.

3) AI for imaging and multimodal decision support (MIAAI Engine) Through our Medical Image Analysis & AI (MIAAI) department, we contribute the development and validation of reproducible AI pipelines for imaging-derived biomarkers. Beyond standard segmentation and radiomics, we offer specialized expertise in Multi-Parametric MRI (including Sodium Imaging/23Na and CEST) to detect invisible metabolic biomarkers, particularly relevant for distinguishing sex-specific mechanisms in cardiovascular disease (HORIZON-HLTH-2026-01-DISEASE-11). Our emphasis is on credibility: we provide cross-site evaluation designs, bias checks, and transparent reporting artefacts (model cards) to make results interpretable and audit-ready.

4) Intelligent sensing and NAMs (LiST Engine) For projects targeting the replacement of animal testing (HORIZON-HLTH-2026-01-TOOL-03) or non-invasive diagnostics, our Life Sciences Technology (LiST) department offers a "Bio-Digital" loop. We develop Organ-on-Chip (OoC) platforms with integrated Plasmonic and Aptamer sensors to provide real-time kinetic monitoring of toxicity or stress biomarkers. We lead the "verification logic" often missing in sensor proposals: repeatability, drift analysis, and field-testing methodology that reflects real conditions rather than lab-only performance.

5) Trust, dialogue, and societal uptake (Heracles-Hebe Foundation) Anchored in the DPU ecosystem, the Heracles-Hebe Foundation allows us to organise structured stakeholder engagement and co-creation activities. This is not generic dissemination; it is a structured feedback loop involving clinicians, patients, and public actors to refine requirements and strengthen acceptability. This capability is critical for calls like HORIZON-HLTH-2026-01-STAYHLTH-03 and DISEASE-02 (Youth Mental Health), where trust-building and implementation conditions are explicit evaluation criteria.

Concrete outputs DPU can commit to

• Living-Lab Validation Package: Workflow integration plans, usability/human-factors study reports, implementation KPIs, and a "deployment playbook" grounded in practice.

• Evidence & Validation Blueprint: Intended-use framing, claims-to-tests traceability, comparator strategy, and audit-ready reporting templates.

• Reproducible AI Evaluation Suite: Evaluation scripts, cross-site reporting, drift/uncertainty monitoring plans, and transparency artefacts (model cards/dataset statements).

• Sensor Verification & Benchmarking Plan: Repeatability/drift testing, confounder matrices, reference procedure mapping, and field validation methodology.