A trio of studies highlights how AI is moving from screening concepts toward decision support in healthcare. One report describes an audio AI model for asthma that surpasses 96% accuracy, aiming to help overcome limitations of spirometry and auscultation in early care. Separately, a clinical AI approach evaluates correlated sensor networks and addresses timestamp errors—an underlying systems problem that can undermine real-world monitoring reliability. Another study describes an AI framework to predict recovery after cardiac arrest from clinical notes, addressing the high uncertainty clinicians face in early prognostication. Across these efforts, the common thread is not just model performance, but the move toward practical deployment constraints—data quality, clinical documentation variability, and usability under real-world conditions.