From medical GPT to medical agents: application prospects of the argus agent combined with EIT in bedside monitoring of ARDS

Acute respiratory distress syndrome (ARDS) is a common clinical syndrome associated with high mortality in critical care medicine, characterized by marked intrapulmonary spatial heterogeneity; static indices derived from laboratory tests, respiratory mechanics, and conventional imaging cannot continuously capture the dynamic physiological process of regional ventilation-perfusion mismatch. Although electrical impedance tomography (EIT) offers noninvasive, radiation-free, continuous bedside visualization of regional ventilation distribution, a single modality alone cannot fully reflect the dynamic evolution of disease treatment and prognosis. In recent years, medical artificial intelligence has been evolving from medical GPT (generative pre-trained transformer), which is centered on language generation, toward medical agents that integrate multisource perception, rule-based governance, interpretable reasoning, and human-in-the-loop mechanisms. The Argus agent, jointly developed by Zhongshan Hospital, Fudan University and Suzhou Kaonter Medical Technology Co., Ltd., has implemented multisource device connectivity, an ARDS overview dashboard, continuous EIT video and derived-index display, white-box alerts, and a cloud/local report pipeline, providing a medical-agent paradigm for the clinical implementation of spatiotemporal multimodal monitoring. Our analysis suggests that the value of the Argus agent lies not in 'giving automatic answers' but in revealing regional differences, organizing multisource evidence, and supporting explainable decisions. In the future, with the advancement of standardized acquisition, multicenter studies, and lifecycle governance, the Argus agent is expected to evolve from a 'foundational framework for a monitoring-oriented digital twin' into a trustworthy monitoring-oriented digital twin for ARDS.

Key words: medical GPT; medical agent; electrical impedance tomography; acute respiratory distress syndrome; multimodal monitoring; digital twi