Response to “Imaging Data Saves Lives: AI-Enabled Worklist Prioritization as Triage Tool and Learning Engine in Large-Scale Combat Operations”

To the Editor: We thank Schutt et al for their thoughtful response to our article and agree that imaging data and radiology workflows are critical components of a future combat casualty care data ecosystem.1 Their emphasis on AI-enabled worklist prioritization and structured imaging integration highlights another critical domain in which passive data collection may improve both operational triage, system-wide learning, and effective procedural and endovascular intervention during large-scale combat operations (LSCO).2 As the authors correctly note, our original framework primarily focused on passive data capture related to prehospital care, physiologic monitoring, logistics, and operational decision-making, rather than on downstream radiology-specific systems and treatments. Nonetheless, we agree that passive imaging data collection during and beyond the prehospital environment would provide substantial value and should be incorporated into future military trauma architectures. Importantly, however, imaging integration alone should not represent the sole expansion of a more sophisticated combat casualty care data framework. Rather, the future battlefield trauma ecosystem must integrate multiple streams of passively collected, real-time data across the continuum of care, including physiologic monitoring, laboratory values, imaging, medication administration and pharmacy services, operative interventions, logistics (especially blood products), evacuation timelines, definitive surgery, rehabilitation, and ultimately outcomes.3 The true value of these systems lies not in isolated data silos but in creating a unified, longitudinal learning ecosystem that rapidly identifies patterns, informs decision-making, provides feedback, and adapts clinical practice in near real time.