Latest AI and machine learning research in medicolegal for healthcare professionals.
With the development and spread of artificial intelligence, technologies based on the neural networks (for example, large language models) have attracted the most attention as promising methods for analyzing and processing data in various fields. Large language models (LLMs) are systems trained on huge amounts of text data and capable of generating answers to user queries. Examples of well-known L...
While adherence to clinical guidelines improves the quality and consistency of care, personalized healthcare also requires a deep understanding of individual disease models and treatment plans. The structured preparation of medical routine data in a certain clinical context, e.g. a treatment pathway outlined in a medical guideline, is currently a challenging task. Medical data is often stored in d...
The association between electronic health record (EHR) documentation and physician burnout is well-known. A combination of insufficient time to comple...
While advanced care planning (ACP) is an essential practice for ensuring patient-centered care, its adoption remains poor and the completeness of its ...
This study evaluated the usability and effectiveness of an artificial intelligence application for wound assessment and management from a clinician-an...
Excessive documentation burden is linked to clinician burnout, thus motivating efforts to reduce burden. Generative artificial intelligence (AI) poses...
Annotated language resources are essential for supervised machine learning methods. In the clinical domain, such data sets can boost use-case specific...
BACKGROUND: There are various molecular hypotheses regarding Alzheimer's disease (AD) like amyloid deposition, tau propagation, neuroinflammation, and...
BACKGROUND: Alzheimer's disease (AD) is a recognized complex and severe neurodegenerative disorder, presenting a significant challenge to global healt...
AIM: The aim of the work is to provide an overview of the potential application of artificial intelligence in forensic medicine and related sciences, ...
The advent of different realms of computational neurosurgery-including not only machine intelligence but also visualization techniques such as mixed r...
One of the challenges of AI technologies is its "black box" nature, or the lack of explainability and interpretability of these technologies. This cha...
Coronary computed tomography angiography (CCTA) is a noninvasive imaging modality of cardiac structures and vasculature considered comparable to invas...
Gastroenterology is a particularly data-rich field, generating vast repositories of data that are a fruitful ground for artificial intelligence (AI) a...
Integrating Artificial Intelligence (AI) and robotics in healthcare heralds a new era of medical innovation, promising enhanced diagnostics, streamlin...
The introduction of novel medical technology, such as artificial intelligence (AI), into traditional clinical practice presents legal liability challe...
Artificial intelligence (AI) could revolutionise health care, potentially improving clinician decision making and patient safety, and reducing the imp...
OBJECTIVE: In this study, we used artificial intelligence (AI) technology to explore for automated medical record quality control methods, standardize...
Social determinants of health (SDoH) are known to impact the health and well-being of patients. However, information regarding them is not always col...