Editorial: Artificial intelligence for smart health: learning, simulation, and optimization

Abstract

Rovati et al. evaluated the usability, workload, and acceptance of a digital twin application designed to simulate patient clinical trajectories based on EHR data for critical care education. Tested with 35 first-year internal medicine residents, the application demonstrated good usability and low to moderate workload. Residents expressed interest in using the digital twin application for ICU training and suggested improvements in clinical fidelity, interface design, learning experience, gaming elements, and implementation strategies. Trevena et al. developed a graph-based patient simulation application designed to model critically ill patients with sepsis. The authors utilize directed acyclic graphs to represent the complex physiological and medication interactions during the first 6 hours of critical illness. Their system consists of three core components: a cross-platform frontend for clinicians and trainees, a cloud-hosted simulation engine, and a graph database to determine the progression of each simulation. The simulation architecture demonstrates the potential to help train future generations of healthcare professionals and facilitate clinicians' bedside decision-making.Wang et al. developed a three-phase methodology for emotion recognition from electroencephalography signals. Their framework addresses the challenges of capturing the complex, nonlinear, and nonstationary dynamics of brain activity by integrating manifold embedding, multilevel heterogeneous recurrence analysis, and ensemble learning. Evaluated on the SJTU-SEED IV database, their method demonstrates superior performance compared to existing commonly used techniques.Meyers et al. investigated the sources of variability affecting operating room (OR) efficiency. The OR process was segmented into eight stages to quantify key process times, such as procedure duration and start time delay. The authors developed linear mixed models to evaluate the effects of factors such as the primary surgeon, anesthesia provider, and procedure type on OR efficiency. This study emphasizes the importance of segmenting the OR process into finer stages for better understanding of efficiency.Finally, we extend our sincere gratitude to the reviewers for their thoughtful and constructive feedback on the manuscripts submitted to this Research Topic. Their insightful evaluations have significantly contributed to enhancing the quality and impact of this collection.

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