Explainable perioperative risk prediction Machine-learning models that predict postoperative infection from routine hospital data, with risk attributions clinicians can read and act on. Time-resolved modelling of the perioperative course Using intraoperative vital-sign dynamics and the timing of events to tell when, during and after surgery, risk can be acted on. Federated learning and synthetic data across hospitals Training and validating clinical models across institutions without moving patient data, and generating synthetic perioperative data that can be shared. LLMs for multilingual clinical data Large language models and semantic embeddings that align medical procedure terms across languages and national coding systems. Equity and deployment-readiness audits Checking whether a clinical model is calibrated and useful for every patient subgroup, and how much the data can actually tell us, before it reaches the ward.