Six methodological commitments shared by every disease module. Foundation models on their own are brittle. Mechanistic simulators on their own are blind. AURORA insists on both — with ethics auditing inside the training loop.
Foundation models, mechanistic simulators and causal graphs are co-designed and share the same provenance pipeline.
Per-module vision-language-omics transformers, pre-trained on permissively-licensed neuroimaging, surgical-video, and biomedical literature corpora. Distilled to clinic-edge sizes so they run on hospital hardware. Conditioned on patient-level signals — never on identity.
PDE solvers in Rust and CUDA for diffusion-reaction, CSF compartment, and biomechanical problems. Differentiable where useful for inverse problems. Tightly coupled to the foundation models through learned priors so the two stay in conversation, not in opposition.
Counterfactual graphs over treatments, resection extents and progression. Trial-aware exclusion of confounded comparisons; Bayesian uncertainty surfaced in every recommendation. The clinician sees the band, not just the number.
AURORA is research and decision-support infrastructure. It is not a medical device, it is not a regulator-approved diagnostic, and it is not a replacement for clinical judgement. Modules destined for clinical use are validated and submitted to regulators by partner institutions — the underlying code remains open.
Federated learning does not eliminate privacy risk. We treat it as one tool among several: differential-privacy budgets per registry, signed updates, and audit logs are the others. Equity audits inside the training loop reduce, but do not erase, the harms of biased data. We publish failures alongside successes.
Finally: not every recommendation a model produces should be acted on. Every output explains itself. Every override is logged. The clinician is the override.