Latest AI and machine learning research in patient safety / risk management for healthcare professionals.
Spatial lifecourse epidemiology is an interdisciplinary field that utilizes advanced spatial, location-based, and artificial intelligence technologies to investigate the long-term effects of environmental, behavioural, psychosocial, and biological factors on health-related states and events and the underlying mechanisms. With the growing number of studies reporting findings from this field and the...
Bipolar disorder (BPD) is often confused with major depression, and current diagnostic questionnaires are subjective and time intensive. The aim of this study was to develop a new Bipolar Diagnosis Checklist in Chinese (BDCC) by using machine learning to shorten the Affective Disorder Evaluation scale (ADE) based on an analysis of registered Chinese multisite cohort data. In order to evaluate the ...
: To address the question of whether antibiotic therapy can obviate the need for prostate biopsy (PBx) in patients presenting with high prostate-speci...
BACKGROUND: For robots to be effectively used in health applications, they need to display appropriate social behaviors. A fundamental requirement in ...
BACKGROUND: In recent months, multiple publications have demonstrated the use of convolutional neural networks (CNN) to classify images of skin cancer...
OBJECTIVE: Virtual reality simulators track all movements and forces of simulated instruments, generating enormous datasets which can be further analy...
Diets rich in omega-3 fatty acids (n-3 FA) have been associated with several health benefits. With the increased interest in n-3 FA both scientificall...
Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the proc...
Molecular genetics laboratory reports are multiplying and increasingly of clinical importance in diagnosis and treatment of cancer, infectious disease...
BACKGROUND: Translational research is a key area of focus of the National Institutes of Health (NIH), as demonstrated by the substantial investment in...
With the advances of machine learning algorithms and the pervasiveness of network terminals, the online medical prediagnosis system, which can provide...
Abnormal connectivity patterns have frequently been reported as involved in pathological mental states. However, most studies focus on "static," stati...
The study objective was to apply machine learning methodologies to identify predictors of remission in a longitudinal sample of 296 adults with a prim...
In this paper, we present a new idea to analyze facial expression by exploring some common and specific information among different expressions. Inspi...
Large language models (LLMs) are increasingly used in scientific writing, but the conditions under which they can be applied responsibly to evidence s...
Large language models now answer medical questions with expert-level performance. However, the context these systems act on can be misleading, and mis...
Large language models perform well on medical examinations, but users routinely challenge their answers and invoke professional roles, and it is uncle...
Background: Systematic reviews of clinical prediction models increasingly include studies using artificial intelligence (AI) and machine learning (ML)...
Background. Cardiovascular-outcomes trials are lengthy, costly, and associated with substantial uncertainty prior to readout. In-silico trial simulati...
Recent offline reinforcement learning (RL) studies report policies that outperform physician decisions on clinical outcomes. We conduct a systematic, ...