As telehealth achieves a steady state in the post–Covid-19 pandemic health care landscape, health systems require operational models that can support clinicians across service lines. The authors describe a highly successful case study of a centralized telehealth support model, the Virtual Care Center (VCC), within Duke University Health System, a large academic health system. Duke…
Indoor fungal contamination poses significant risks to public health, yet existing detection methods are slow, costly, and lack spatial resolution. Conventional approaches rely on laboratory analysis or high-concentration sampling, making them unsuitable for real-time monitoring and scalable deployment. We introduce \textbf{\textit{Scensory}}, a robot-enabled olfactory system that simultaneously identifies fungal species and localizes their spatial origin…
We present Sym2Real, a fully data-driven framework that provides a principled way to train low-level adaptive controllers in a highly data-efficient manner. Using only about 10 trajectories, we achieve robust control of both a quadrotor and a racecar in the real world, without expert knowledge or simulation tuning. Our approach achieves this data efficiency by…
The advent of digital twins has increased the demand for longer-duration simulations that span multiple physiological states. Digital twins have emerged as powerful tools in cardiovascular modeling, enabling patient-specific simulations of coronary blood flow for noninvasive diagnosis and treatment planning. Although these simulations achieve high fidelity under steady or periodic heart rates, modeling real-world transitions,…
BACKGROUND AND PURPOSE: Intracranial aneurysms treated with endovascular embolization often require surveillance imaging using digital subtraction angiography, an invasive, risky, and expensive procedure. Existing non-invasive imaging modalities (standard computed tomography [CT] or magnetic resonance [MR] angiography) are often unsatisfactory for evaluating treated aneurysm due to artifacts from embolization devices. The objective of the present study was…
Risk assessments for a pediatric population are often conducted across multiple stages. For example, clinicians may evaluate risks prenatally, at birth, and during Well-Child visits. Although predictions made at later stages typically achieve higher precision, it is clinically desirable to make reliable risk assessments as early as possible. Therefore, this study focuses on improving prediction…
Training reinforcement learning agents with human feedback is crucial when task objectives are difficult to specify through dense reward functions. While prior methods rely on offline trajectory comparisons to elicit human preferences, such data is unavailable in online learning scenarios where agents must adapt on the fly. Recent approaches address this by collecting real-time scalar…
This workshop focuses on ethics education with emergent technologies that emphasize the future role of engineers to responsibly innovate, develop, and deploy for various shareholders. Our team of Duke faculty from biomedical engineering, mechanical engineering, and electrical & computer engineering have successfully integrated ethics education modules in biotechnology, robotics, automation, and machine learning within technical…
Digital twins offer a promising approach to advancing healthcare by providing precise, noninvasive monitoring and early detection of diseases. In heart failure (HF), a leading cause of mortality worldwide, they can improve patient monitoring and clinical outcomes by simulating hemodynamic changes indicative of worsening HF. Current techniques are limited by their invasiveness and lack of…
Large language models (LLMs) have performed well across various tasks in clinical natural language processing tasks, despite not being directly trained on electronic health record (EHR) data. In this work, we examine how popular open-source LLMs learn clinical information from large mined corpora through two crucial but understudied lenses: (1) their interpretation of clinical jargon,…