AIRIS will develop the next generation of multimodal Generative AI (GenAI) models to accelerate research on predictive and personalised medicine. Building on recent advances in LLMs, causal inference and mechanistic modelling, AIRIS will integrate heterogeneous biomedical data (omics, imaging, clinical, laboratory, lifestyle, PROMs and scientific literature) into mechanism-informed generative frameworks. These models will not only generate biologically plausible synthetic data to address sparsity and bias but also embed causal and dynamical constraints across biological scales, from “virtual cells” to multi-organ models, enabling counterfactual reasoning and in-silico hypothesis testing. The project will deliver (i) robust agentic multimodal data integration pipelines; (ii) novel mechanism-anchored generative architectures with interpretability and bias-mitigation safeguards; (iii) hypothesis generation assistants for biomarker discovery, drug repurposing, and intervention design; and (iv) a scalable MLOps infrastructure ensuring reproducibility. AIRIS will be validated using already existing large-scale datasets in five high-impact use cases including Pulmonary Fibrosis, Steatotic Liver Disease, Cardiovascular Disease, Chronic Kidney Disease and Inflammatory Bowel Disease. AIRIS will demonstrate utility in modelling of disease, while also allowing cross-disease insights. A multidisciplinary consortium spanning leading universities and research centres, HPC experts, industry, and SME ensures excellence across the project dimensions. ELSI and fairness will be systematically addressed by SSH and legal experts. Outputs will be openly disseminated through benchmarks, training, and open-source releases, fostering European leadership in mechanism-informed GenAI. AIRIS will maximise scientific, clinical, and societal impact by bridging AI innovation with biomedical discovery, improving disease understanding, and supporting equitable personalised care.