A recent study published June 12 in the Journal of the American College of Surgeons demonstrates that large language models can significantly enhance prehospital trauma triage accuracy, particularly in pediatric emergencies. Researchers from the Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, operating through its Data Augmented Research Technology in Surgery laboratory, investigated how artificial intelligence could mitigate the high-stakes communication challenges inherent in emergency medical services handoffs. Prehospital triage relies on brief, often chaotic radio or telephone reports from paramedics. Time pressure, background noise, and physiological nuances frequently lead to undertriage, where severe injuries go unrecognized, or overtriage, which strains hospital resources. Pediatric cases present unique difficulties, as children compensate for trauma longer than adults, masking critical internal bleeding behind seemingly stable vital signs. Additionally, paramedics encounter pediatric emergencies less frequently, reducing their pattern recognition for these cases. To address these gaps, the Buffalo research team evaluated an LLM using 133 pediatric emergency department activations. The model was tasked with processing raw EMS call transcripts, filtering out the 98 percent of nonmedical language, and extracting clinically relevant data such as injury mechanisms, vital signs, and mental status. The system then generated structured summaries and recommended triage levels. The results indicated that the LLM successfully compressed call transcripts by approximately 80 percent while maintaining clinical fidelity. Its independent triage accuracy matched that of experienced trauma staff. However, the most significant finding emerged from the human-AI interaction dynamics: when physicians reviewed the LLM recommendations after making an initial triage error, the model prompted them to correct their decisions in 73 percent of cases, effectively tripling the odds of accurate intervention. Lead authors and project leads, including Dr. Peter C. W. Kim, Dr. Ascharya K. Balaji, and Dr. Brendan T. Fox, emphasize that the technology functions as a real-time cognitive aid rather than an autonomous decision-maker. The system operates as a communication-aware assistant, continuously parsing incoming EMS reports, stripping away contextual noise, and delivering actionable intelligence directly to receiving physicians. Clinical oversight remains paramount, with providers retaining the authority to accept, modify, or override AI suggestions. By delegating the signal-processing burden to machine learning algorithms, hospitals can preserve valuable response time and reduce cognitive fatigue among trauma teams. As emergency departments continue to face increasing patient volumes and communication bottlenecks, integrating LLMs into prehospital workflows offers a scalable method to standardize information transfer, minimize human error, and ultimately accelerate life-saving interventions for the most vulnerable populations.