OBJECTIVE: This perspective explores how ambient artificial intelligence (AI) scribes could support documentation and quality improvement (QI) of structured, team-based provider-to-provider communication in acute care settings. BACKGROUND: In acute care settings, team-based discussions such as multidisciplinary rounds and handoffs are essential to the delivery of safe care. These discussions rely on standardized frameworks (eg, IPASS, checklists) to ensure consistent information transfer and shared understanding. Despite their importance, these verbal discussions are often incompletely documented or left undocumented in the electronic health record, leading to gaps in clinical narrative, difficulty in QI evaluation, and lost opportunities for organizational learning. APPROACH: We outline how ambient AI scribes could enhance documentation of team-based communication in daily rounding and handoff discussions. We examine key sociotechnical challenges, including workflow integration, multiprovider consent, surveillance concerns, and vendor collaboration. We describe our experience with proof-of-concept demonstrations as an early feasibility signal. RESULTS: Ambient AI scribes are a promising tool for capturing structured team communication. Their use should be explored for its potential to improve documentation, support clinician well-being, and enable data-driven approaches to QI and communication fidelity assessments. Effective implementation requires workflow adaptations incorporating scribe output verification, transparent governance, and trust-building efforts to ensure clinician acceptance. DISCUSSION: Ambient AI scribes represent a novel frontier in documentation of structured team discussions in acute care settings, with the potential to strengthen communication reliability and systems learning of these vital conversations. Future research should evaluate their impact on patient safety, workforce well-being, and patient outcomes in acute care settings.

Accelerated by telemedicine, advances in Remote Photoplethysmography (rPPG) are beginning to offer a viable path toward non-contact physiological measurement. Unfortunately, the datasets for rPPG are limited as they require videos of the human face paired with ground-truth, synchronized heart rate data from a medical-grade health monitor. Also troubling is that the datasets are not inclusive of diverse populations, i.e., current real rPPG facial video datasets are imbalanced in terms of races or skin tones, leading to accuracy disparities on different demographic groups. This paper proposes a scalable biophysical learning based method to generate physio-realistic synthetic rPPG videos given any reference image and target rPPG signal and shows that it could further improve the state-of-the-art physiological measurement and reduce the bias among different groups. We also collect the largest rPPG dataset of its kind (UCLA-rPPG) with a diverse presence of subject skin tones, in the hope that this could serve as a benchmark dataset for different skin tones in this area and ensure that advances of the technique can benefit all people for healthcare equity. The dataset is available at https://visual.ee.ucla.edu/rppg_avatars.htm/.

With the resurgence of non-contact vital sign sensing due to the COVID-19 pandemic, remote heart-rate monitoring has gained significant prominence. Many existing methods use cameras; however previous work shows a performance loss for darker skin tones. In this paper, we show through light transport analysis that the camera modality is fundamentally biased against darker skin tones. We propose to reduce this bias through multi-modal fusion with a complementary and fairer modality - radar. Through a novel debiasing oriented fusion framework, we achieve performance gains over all tested baselines and achieve skin tone fairness improvements over the RGB modality. That is, the associated Pareto frontier between performance and fairness is improved when compared to the RGB modality. In addition, performance improvements are obtained over the radar-based method, with small trade-offs in fairness. We also open-source the largest multi-modal remote heart-rate estimation dataset of paired camera and radar measurements with a focus on skin tone representation.