UBITECH is proud to announce its participation in GENz-trials, an ambitious new project funded under the Innovative Health Initiative Joint Undertaking (IHI JU) under Grant Agreement No. 101252995. The project officially launched with its Kick-off Meeting held on 26–27 February 2026 in Thessaloniki, Greece, hosted by the coordinating partner Aristotle University of Thessaloniki (AUTH). Spanning 48 months (January 2026 – December 2029), GENz-trials brings together a multidisciplinary European consortium committed to fundamentally rethinking the clinical trial paradigm through data science, AI automation, and simulation technologies. As a matter of fact, UBITECH participates in this next-generation IHI-funded clinical trial initiative that harnesses federated AI, digital twins, and synthetic control arms to transform how clinical research is designed, conducted, and evaluated across Europe.
Clinical trials are the cornerstone of evidence-based medicine, yet they remain notoriously slow, costly, and exclusive. GENz-trials proposes a transformative framework built around three interlinked innovation pillars that address these longstanding inefficiencies. The first pillar focuses on the automation of critical trial workflows. By leveraging generative AI, the project targets the most time-consuming administrative burdens in clinical research — including the drafting of personalized informed consent documents, protocol authoring, continuous monitoring, and regulatory submissions — with the goal of significantly expediting trial initiation and reducing overhead for research teams. The second pillar introduces AI-driven patient recruitment through deep analysis of large-scale Electronic Health Records (EHRs) and real-world datasets. By intelligently matching patients to trial protocols, GENz-trials aims to improve enrolment rates, foster inclusiveness, and drastically reduce patient dropout — challenges that have historically undermined the statistical power and representativeness of clinical evidence. The third pillar advances the integration of Digital Twins (DTs) and Synthetic Control Arms (SCAs) for protocol optimization via in-silico simulation. By constructing robust real-world data comparators, GENz-trials enables ethically sound alternatives to traditional placebo-controlled groups, opening the door to a more ethical, inclusive, and cost-effective model for clinical research.
Within GENz-trials, UBITECH takes on a pivotal multi-faceted technical role driven by its Big Data Engineering, Analytics & Science (DEA) Research Group (https://ubitech.eu/dea/). The team’s contributions span the full lifecycle of the project’s technology stack, from privacy-preserving infrastructure through to explainability, validation, and platform delivery. UBITECH will design and implement a scalable, GDPR-compliant federated learning infrastructure that enables privacy-preserving patient recruitment and eligibility screening across distributed hospital data sources. Building on open-source frameworks such as Flower, and extended with custom modules that interface with OMOP-CDM-formatted data and local EHR systems, the infrastructure will support dual-encoder AI architectures for real-time patient-trial matching. These models combine eligibility criteria embeddings — parsed from protocols via NLP — with vectorized representations of structured and unstructured patient records, enabling precise and scalable recruitment across diverse healthcare settings.
As the main developer of the GENz-trials platform, UBITECH is responsible for integrating all project tools into a secure, modular, and interoperable cloud-based environment. This platform will interface with synthetic control arm engines and harmonize the federated recruitment infrastructure with AI-powered workflow optimization tools, enabling a cohesive end-to-end trial planning and execution environment for clinicians and researchers alike.
Beyond development, UBITECH leads the implementation of trust, validation, and explainability components for the Digital Twin-based Synthetic Control Arms. A comprehensive validation and explainability layer will ensure that DT-based SCAs are interpretable, causally robust, and aligned with regulatory expectations. Model transparency will be achieved through visual explanation techniques such as Integrated Gradients, attention maps, and counterfactual explanation models. To assess consistency, simulated patient trajectories will be cross-validated against real-world observational data using the SIMCor toolkit, employing metrics such as distributional similarity, endpoint coverage, and robustness under protocol perturbations.
“GENz-trials represents a significant step forward in our mission to apply advanced AI and federated learning technologies to pressing real-world healthcare challenges. At UBITECH’s DEA Research Group, we are committed to engineering trustworthy, privacy-preserving solutions that do not merely optimize clinical workflows but fundamentally improve the quality and inclusivity of clinical evidence. We look forward to collaborating with our outstanding consortium partners to deliver a platform that can genuinely transform how healthcare innovations are assessed and brought to patients.”
— Dr. Kostas Perakis, Head, Big Data Engineering, Analytics & Science (DEA) Research Group, UBITECH
GENz-trials (Grant Agreement No. 101252995) is supported by the Innovative Health Initiative Joint Undertaking (IHI JU) under the European Union’s Horizon Europe research and innovation programme, with co-funding from COCIR, EFPIA, Europa Bío, MedTech Europe, and Vaccines Europe. The project runs from January 2026 to December 2029. For further information, visit https://genz-trials.eu/ or consult the IHI project factsheet.
