Latest AI and machine learning research in domestic violence for healthcare professionals.
Surgical scene understanding (SSU) describes the use of Artificial Intelligence (AI) to provide an understanding of visual components of surgical imaging data, such as laparoscopic surgery videos. While hundreds of publications report AI capabilities to identify instruments, anatomical structures, and other contextual data and testify potential for real-time support in the operating room, the clin...
Artificial intelligence (AI) is increasingly embedded in clinical trials, yet poor reproducibility remains a critical barrier to trustworthy and transparent research. In this study, we propose a structured calibration of the CONSORT-AI reporting guideline using the FAIR (Findable, Accessible, Interoperable, Reusable) principles. We introduce the application of CALIFRAME, a framework designed to ev...
The lack of understanding of how individuals communicate suicidal stress hinders global suicide intervention plans and practices. This study identifie...
Advancements in imaging technology, alongside increasing longevity and co-morbidities, have led to heightened demand for diagnostic radiology services...
Systematic review screening is often burdensome, prone to human error, and requires significant manual effort. Synthesa AI, a large language model (LL...
The early detection of adverse drug events (ADEs) became a critical issue in clinical research after the thalidomide disaster in 1961, which resulted ...
Musculoskeletal conditions are a leading global cause of disability, yet the factors influencing long-term musculoskeletal health, particularly follow...
Abstract screening is a notoriously labour-intensive step in systematic reviews. AI-aided abstract screening faces several grand challenges, such as t...
Computer-aided detection (CAD) software analyzes chest X-rays for features suggestive of tuberculosis (TB) and provides a numeric abnormality score. H...
Zidovudine (AZT), a key antiretroviral drug used for HIV treatment and preventing mother-to-child transmission, has insufficient post-marketing pharma...
Tuberculosis (TB) remains a leading global cause of preventable death, with 10.8 million cases and 1.3 million deaths reported in 2023. Current method...
Interest in the use of prediction models to support referrals to palliative care is surging. Few high-performing models have been developed, implement...
To identify post-marketing adverse event (AE) signals associated with isotretinoin using real-world data from the U.S. Food and Drug Administration (F...
Lung cancer remains the leading cause of cancer-related mortality in the United States, with screening adherence rates below 16% nationally and even l...
Opioid misuse remains a critical public health concern, associated with increased risk of overdose, psychiatric comorbidity, and societal costs. While...
To develop AutoReporter, a large-language-model system that automates evaluation of adherence to research reporting guidelines. Eight prompt-engineeri...
Pathology reporting of colorectal cancer (CRC) follows the International Collaboration on Cancer Reporting (ICCR) guidelines which define a set of 25 ...
Lung cancer (LC) is the leading cause of cancer mortality, often diagnosed at advanced stages. Screening reduces mortality in high-risk individuals, b...
Bronchopulmonary dysplasia (BPD) and pulmonary hypertension (PH) are leading causes of morbidity and mortality in premature infants. To determine whet...
Prostate cancer diagnosis and treatment planning depend on accurate histopathological assessment of needle biopsies, particularly through the Gleason ...