Generative AI and Virtual Human Twins for digital clinical trials

We are interested in collaborative projects combining generative artificial intelligence, multimodal biomedical data and Virtual Human Twin technologies to develop next-generation approaches for digital or in silico clinical trials and personalized treatment selection.

Our interest is in creating patient-specific computational representations that integrate genomic and other omics profiles with clinical, laboratory, imaging and longitudinal health data. Generative and predictive AI models could then be used to model disease trajectories, estimate individual treatment responses, explore alternative therapeutic scenarios and generate virtual patient populations for the evaluation of clinical hypotheses.

An important objective is to move beyond population-level prediction toward patient-specific simulation and decision support. Such approaches could contribute to trial enrichment, identification of likely responders and non-responders, assessment of treatment combinations, generation of synthetic control cohorts and optimization of clinical trial design.

We can contribute expertise in genomics, transcriptomics, single-cell sequencing, multi-omics data analysis and bioinformatics, and are interested in participating in the development and biological validation of AI-driven patient models.

We are seeking partners in generative AI, foundation models, Virtual Human Twins, clinical informatics, mathematical and mechanistic modeling, clinical trial methodology, data harmonization, explainable AI and clinical validation.