Triple innovations in acute critical illness management: integrating AI early warning, precision biomarkers, and telemedicine

Emergency medicine, as a critical component of the modern healthcare system, is tasked with the rapid diagnosis and treatment of patients with acute critical illnesses and time-sensitive medical emergencies. However, traditional emergency management models face numerous challenges, including disease complexity, uneven resource allocation, and time sensitivity. In recent years, artificial intelligence (AI), precision medicine, and telemedicine have driven substantial innovation in acute critical illness management. AI early-warning systems integrate multisource data to support risk stratification across time-sensitive cardiovascular, infectious, traumatic, and toxicological conditions. Precision biomarker testing can accelerate diagnosis and inform targeted treatment, while multimarker strategies may improve diagnostic discrimination. Telemedicine, including 5G-enabled emergency networks and longitudinal management loops, can extend specialist expertise across prehospital, emergency department, and inpatient settings. Despite these advances, implementation remains constrained by limited prospective validation, variable actionability, workflow integration, interoperability, and equity. This narrative review examines how AI early warning, precision biomarkers, and telemedicine can be integrated across acute critical illness management, with emphasis on cardiovascular and cerebrovascular emergencies, infectious diseases, trauma, and poisoning, and identifies priorities for clinically meaningful evaluation.