Projects
Building the future of predictive medicine
Our projects translate advanced computational biology and AI into real-world impact, addressing unmet medical needs through innovation, collaboration, and scientific excellence.
Smart pathology slide scanner for diagnosis and patient-specific treatment recommendation in oncology
CancerScan is an EIC Pathfinder-funded project developing a next-generation AI-powered medical platform that creates patient-specific tumour digital twins to support personalised cancer treatment.. The project addresses one of oncology’s greatest challenges: predicting how an individual patient will respond to therapy before treatment begins. Its initial clinical focus is pancreatic cancer, one of the deadliest malignancies, where treatment decisions are often made with limited ability to anticipate therapeutic response or resistance.
The CancerScan platform combines digital pathology, multi-omics profiling, medical imaging, organoid experiments, and clinical data within a unified computational framework.
By modelling the tumour microenvironment (TME), which is a complex network of cancer, immune, stromal, and endothelial cells surrounding a tumour, the system aims to understand how cellular interactions influence treatment outcomes. These biological mechanisms are captured in patient-specific digital twins capable of simulating tumour behaviour and predicting responses to different therapeutic strategies.
A distinguishing feature of CancerScan is the integration of AI with mechanistic biological modelling. The project develops biomedical knowledge graphs, analyses tumour communication networks, and applies advanced computational models to learn how tumour microenvironments evolve under treatment pressure. By embedding these capabilities into an innovative digital pathology slide scanner, CancerScan seeks to provide clinicians with an explainable decision-support tool that can help identify the right treatment for the right patient at the right time.
The project brings together expertise in computational biology, artificial intelligence, pathology, systems biology, and hardware engineering through a multidisciplinary European consortium.
In recognition of its potential to transform precision oncology, the CancerScan Digital Twin System has received an EMA Breakthrough Device designation through the EMA Breakthrough Medical Devices Pilot. The designation recognises the potential of the CancerScan Digital Twin System to address one of the most significant unmet medical needs in contemporary oncology: the absence of a validated, individualised treatment selection tool for patients with pancreatic ductal adenocarcinoma.
TME analysis
Identifying and classifying key components of the tumour microenvironment.
Therapy response prediction
Simulating how an individual tumour may respond to different treatment options.
Clinical decision support
Providing structured insights for pathologists and oncologists.
Drug development support
Helping predict drug efficacy in preclinical and clinical research.
Advancing Breast Cancer Individualised Generative AI for Longitudinal Outcomes
ABIGAIL4D is developing a 4D Generative AI system to transform personalised treatment for early breast cancer.
By integrating longitudinal multimodal data—including medical imaging, pathology, genomics, and clinical information—the platform learns how disease evolves over time and predicts tumour response, survival, and treatment toxicity.
Through specialised AI agents and counterfactual analysis, ABIGAIL4D estimates the potential benefits and risks of different treatment strategies, providing clinicians with transparent, explainable decision support while keeping them fully in control. The project also incorporates trust-by-design principles, regulatory readiness, and validation in multi-centre clinical settings.
Beyond improving therapy selection and reducing unnecessary procedures, ABIGAIL4D lays the foundation for applying responsible AI-driven precision medicine to other solid tumours.
The project starts in October 2026.
Disease evolution modelling
Modelling how breast cancer may change and progress during treatment and follow-up.
Combining patient data
Bringing together imaging, pathology, genomics, and clinical information into one connected view of the patient.
Comparing treatment options
Estimating how different treatment strategies may affect an individual patient’s outcome, benefit, and risk.
Multi-agent decision support
Using specialised AI agents to generate explainable recommendations with quantified uncertainty.
FUTURE RESEARCH INNITIATIVES
A selection of Neovivum-led research concepts currently under review for future funding innitiatives.
CROWNED
Digital twins for rational nanomedicine design
Developing AI-powered approaches to predict how protein corona formation affects nanoparticle targeting, enabling more efficient and cost-effective nanomedicine development.
MOSAIC
Multi-organ digital twins for translational medicine
Combining organ-on-chip systems and AI-powered digital twins to model complex interactions between organs and improve prediction of treatment efficacy and safety.
MINDSET
Programmable bioelectric tissue regeneration
Exploring magnetically controlled bioelectric signalling to direct tissue repair and accelerate wound healing through precision regenerative medicine.
SOFTWIN
Research capacity and soft robotics innovation
Building advanced capabilities in soft robotics, multi-material fabrication, and unconventional computing through international collaboration and knowledge transfer.
SCIENTIFIC FOUNDATIONS
Neovivum builds on over a decade of research led by its CEO Igor Balaž at the University of Novi Sad. These projects established the scientific and technological foundations that continue to shape the company’s research today. Each project represents an incremental expansion of scope, from nano-scale evolutionary design to systems-level biohybrid integration, all unified by a central insight about how biological and computational systems evolve.
Horizon 2020 (2018-2022)
Evolvable platform for programmable nanoparticle-based cancer therapies
Horizon 2020 (2020-2022)
Platform for Rapid Development of Personalized Nanomedicine Drug Delivery Systems
Horizon Europe (2022-2026)
A Modular Framework for Designing and
Producing Biohybrid Machines
The first Neovivum project
At the inaugural Serendipity Collective event in Berlin (2023), Igor Balaž and his team were awarded a 50,000$ grant from the Office of Naval Research Global (ONRG) to develop a new generation of AI systems capable of advanced reasoning, adaptation, and self-monitoring. The project, Bio-synthetic Lifeforms: A Framework for Generating Evolvable Digital Metacognitive Agents, explored how artificial intelligence could move beyond narrow task-specific models toward systems capable of understanding complex environments, generating hypotheses, learning continuously, and reasoning across multiple domains.
During this period, the team created the foundations of a digital-agent platform designed to integrate multimodal information, reason across diverse knowledge domains, and model complex dynamic systems. These concepts would later become central not only to Neovivum’s vision of predictive biology, but also to future applications in advanced manufacturing, soft robotics, and intelligent decision-support systems.