Emerging technologies and AI-assisted tools in cardiopulmonary monitoring

Purpose of review Cardiopulmonary monitoring is fundamental to critical care, yet traditional approaches rely on simplified thresholds that capture only a fraction of the rich information contained within waveforms, imaging, and continuous physiological data. This review examines emerging applications of artificial intelligence (AI) and machine learning (ML) that enhance waveform interpretation, automate point-of-care ultrasound (POCUS), enable predictive monitoring, and extend advanced assessment capabilities into low-resource settings. Recent findings AI models now identify deterioration earlier than conventional tools, derive complex hemodynamic variables from noninvasive signals, and predict events such as hypotension, cardiac arrest, and sepsis hours in advance. In POCUS, AI enables real-time acquisition guidance and automated cardiac and pulmonary interpretation, allowing novice users to obtain expert-quality studies. Cloud and edge-based architectures further support AI-driven monitoring in austere environments. Despite these advances, most AI systems remain in early development; fewer than 2% have undergone clinical integration, and challenges persist related to generalizability, bias, heterogeneous data quality, and limited prospective evaluation. Summary AI-assisted cardiopulmonary monitoring has the potential to transition critical care from reactive assessment to dynamic, anticipatory management. Realizing this promise will require rigorous validation, workflow integration, and evidence demonstrating true clinical benefit.