Conclusions

The current literature indicates that real-time AI can identify patients at risk of sepsis earlier than conventional recognition pathways, with many contemporary models demonstrating strong discriminatory performance and clinically meaningful lead times. However, the most important challenge is no longer whether sepsis can be predicted, but whether these predictions can be delivered in a manner that is trustworthy, transferable, and capable of improving patient outcomes across diverse care settings. The evidence reviewed suggests that progress in model architecture has outpaced progress in prospective validation, calibration reporting, workflow integration, and demonstration of consistent clinical benefit. Future success will therefore depend on shifting emphasis from isolated performance metrics toward implementation-ready systems that combine accuracy with interpretability, external validity, and measurable reductions in morbidity and mortality. The central take-home message is that the next generation of sepsis AI should be judged not by how well it predicts sepsis in retrospective datasets, but by how reliably it improves care at the bedside.