Latest AI and machine learning research in medicolegal for healthcare professionals.
OBJECTIVES: Adverse event detection from Electronic Medical Records (EMRs) is challenging due to the low incidence of the event, variability in clinical documentation, and the complexity of data formats. Pulmonary embolism as an adverse event (PEAE) is particularly difficult to identify using existing approaches. This study aims to develop and evaluate a Large Language Model (LLM)-based framework ...
IMPORTANCE: The increase of electronic health record (EHR) work negatively impacts clinician well-being. One potential solution is incorporating an ambient artificial intelligence (AI) documentation platform.
BACKGROUND: Good medical records are an essential part of healthcare. However, the burden of clinical documentation can reduce clinician productivity ...
The automatic summarization of surgical videos is essential for enhancing procedural documentation, supporting surgical training, and facilitating p...
Suicide is a critical global public health issue, with millions experiencing suicidal ideation (SI) each year. Online spaces enable individuals to e...
Characterization of atomic-scale materials traditionally requires human experts with months to years of specialized training. Even for trained human...
The governance of frontier artificial intelligence (AI) systems--particularly those capable of catastrophic misuse or systemic failure--requires ins...
OBJECTIVE: Raman spectroscopy is proposed as a next-generation method for the identification of monosodium urate (MSU) and calcium pyrophosphate (CPP)...
BACKGROUND: Robotic systems enhance physicians' capabilities by replicating hand movements in real-time, ensuring precise control and a quick return t...
Pulmonary embolism (PE) is a leading cause of cardiovascular mortality, yet our understanding of optimal management remains limited due to heterogen...
The rapid evolution of Large Vision-Language Models (LVLMs) has highlighted the necessity for comprehensive evaluation frameworks that assess these ...
The transition towards patient-centric healthcare necessitates a comprehensive understanding of patient journeys, which encompass all healthcare exp...
Cardiopulmonary exercise testing (CPET) provides a comprehensive assessment of functional capacity by measuring key physiological variables includin...
Analyzing CT scans, MRIs and X-rays is pivotal in diagnosing and treating diseases. However, detecting and identifying abnormalities from such medic...
Machine Learning (ML) models are widely used across various domains, including medical diagnostics and autonomous driving. To support this growth, c...
Given the substantial growth in the use of additive manufacturing in construction (AMC), it is necessary to ensure the quality of printed specimens ...
Alzheimer's disease (AD) is a major neurodegenerative condition that affects millions around the world. As one of the main biomarkers in the AD diag...
Privacy-preserving medical decision support for kidney disease requires localized deployment of large language models (LLMs) while maintaining clini...
The integration of artificial intelligence (AI) into surgery raises significant ethical concerns, including the impact on autonomy, human authority an...
The exponential growth of unstructured text data presents a fundamental challenge in modern data management and information retrieval. While Large L...