Researchers reported a multicenter large language model that predicts acute kidney injury (AKI) and offers explainable risk attribution across hospital sites. The system is trained on diverse clinical data to maintain robustness across institutions rather than relying on a single-center dataset. The work frames early AKI forecasting as a way to prevent irreversible damage, pairing risk prediction with explainable multicenter analytics. For clinicians, the operational value lies in identifying patients earlier and understanding which factors drive risk. — Key takeaway: multicenter training and explainability are positioned as key design requirements for deployment of medical LLMs in high-stakes settings.