Latest AI and machine learning research in prevention of medical errors for healthcare professionals.
OBJECTIVES: Radiology reports are primarily written for professional communication and may be difficult for patients to understand. We investigated whether AI-based simplification of a single standardized neuroradiology report improves participant-rated communication quality compared with a conventional professional-language report. MATERIALS AND METHODS: In this prospective, randomized, blinded s...
Embodied artificial intelligence may extend surgical robotics beyond teleoperation by enabling robots to interpret natural-language commands, perceive dynamic environments, and generate adaptive physical actions. Surgical instrument exchange provides a clinically recognizable test case requiring communication, visual localization, dexterity, and bidirectional interaction. We developed a voice-inte...
BACKGROUND: Although hip fractures are commonly associated with functional decline, increased morbidity, and mortality, accurate models for both short...
BACKGROUND: As society is increasingly depending on large language models (LLMs) for health-related questions, it is essential to objectively evaluate...
BACKGROUND: Large language models (LLMs) are increasingly used to support digital health communication, yet their reliability in patient-facing cardio...
Conventional medical sensors primarily function as passive transducers and often struggle to maintain accuracy and stability under complex physiologic...
Increasing evidence indicates that cellular senescence, metabolic dysfunction, stromal remodeling, and immune perturbation collectively contribute to ...
BACKGROUND: Motivational interviewing (MI) is widely used in preventive interventions, yet coding MI techniques and monitoring intervention adherence ...
BACKGROUND: The growing integration of personalized risk prediction (PRP) and AI substantially reshapes diagnostic and therapeutic decision-making in ...
INTRODUCTION: We examined whether machine learning identified baseline variables that predicted two-year prevention and remission from anxiety, depres...
BACKGROUND: The advent of artificial intelligence (AI) presents an opportunity to enhance infection prevention practices. However, its use among infec...
BACKGROUND: In end-of-life care (EOL) in the intensive care unit (ICU), intensivists are expected to provide medically appropriate and empathetic comm...
INTRODUCTION: Artificial Intelligence (AI) is transforming dental education (DE) by advancing teaching strategies, clinical training, and patient care...
OBJECTIVES: Evaluate whether general-purpose large language models (LLMs) demonstrate competencies suitable for antimicrobial stewardship (AMS) suppor...
Large language models (LLMs) are increasingly used for patient health education, yet the readability and educational quality of LLM-generated informat...
BACKGROUND: Multicall memory capabilities in AI-powered health care communication systems show promise for enhancing patient engagement, but their imp...
INTRODUCTION: Fracture misdiagnosis is a common diagnostic error in emergency departments (EDs) and minor injury units (MIUs), leading to poor patient...
Alcohol Use Disorder (AUD) is a chronic, relapsing condition, and identifying periods of elevated lapse risk remains a major challenge in supporting r...
Generative AI has transformed the health information ecosystem by enabling scalable, sophisticated health misinformation production at near-zero margi...
Zohny et al provide a proof of concept for large language model (LLM)-patient communication in medical decision-making, discussing some of the risks a...