Latest AI and machine learning research in prevention of medical errors for healthcare professionals.
Digital infrastructure is growing at a rapid pace in the United States, and as a result, exposure to advanced cyber threats to critical sectors including healthcare, finance, transportation, energy and government systems is growing. The traditional cybersecurity approaches, including signature-based intrusion detection systems, have become less effective against today's cyber attacks, as they are ...
Objectives: Older adults with comorbidities and polypharmacy have disproportionately high risk of hospitalization as well as readmission from adverse drug events (ADEs), of which 28%-71% are preventable (pADEs). This paper introduces an LLM application, CommunicADE, designed to support risk-mitigation of pADE-related readmission for the aforementioned population. We aim to evaluate CommunicADE's t...
Background: Long-acting pre-exposure prophylaxis (PrEP) expands HIV prevention options for women. However, PrEP impact depends on addressing persisten...
Background: Non-communicable diseases (NCDs) represent a critical public health challenge in Kenya, responsible for over 50% of inpatient admissions a...
In an era dominated by big data and machine learning, establishing valuable data collaboration has never been more critical. However, such collaborati...
Background: Professionalism and effective communication are foundational determinants of patient safety and quality of care. Unprofessional behaviors ...
Long-horizon clinical simulation -- predicting how a patient's physiology evolves over years under specified interventions -- is central to chronic-di...
Hand-surface interactions between clinicians, patients, and medical equipment play a central role in pathogen transmission during medical procedures. ...
Cardiac rehabilitation is critical for secondary prevention, yet long-term adherence remains low. We present CUOREMA, a new personalized mobile health...
Robust whole-slide image (WSI) analysis under strict data-governance remains challenging due to substantial cross-institutional stain heterogeneity. D...
Virtual Try-On (VTON) aims to synthesize photorealistic images of garments precisely aligned with a person's body and pose. Current diffusion-based me...
Symbols are shared, but perception is private. We study emergent communication between heterogeneous visual agents through decentralized learning, ask...
The rapid development of low-altitude economy has driven the proliferation of Unmanned Aerial Vehicle (UAV) applications, including logistics, inspect...
Standardized patients (SPs) are central to clinical communication training but are constrained by cost, scalability, and reliance on trained actors. W...
Federated fine-tuning provides a practical route to adapt large language models (LLMs) on edge devices without centralizing private data, yet in mobil...
Purpose: Gaze-following, the task of inferring where individuals are looking, has been widely studied in computer vision, advancing research in visual...
This paper presents a computational case study that evaluates the capabilities of specialized machine learning models and emerging multimodal large la...
Large language models are increasingly deployed as autonomous diagnostic agents, yet they conflate two fundamentally different capabilities: natural-l...
Inadequate discharge communication is a well-documented contributor to medication non-adherence, missed follow-ups, and preventable readmissions acros...
The translation of artificial intelligence into clinical practice depends, in large part, on access to data that patients are understandably reluctant...