Latest AI and machine learning research in surveillance for healthcare professionals.
'Stems', which mark pharmacological relationships between substances, form the backbone of the International Nonproprietary Name (INN) system, developed by the WHO in the 1950s. In this paper, we propose using the INN stems to enhance pharmacovigilance signal detection. After analysis of historical cases and current pharmacovigilance practices, we discuss how stem-based classification could facili...
PURPOSE: The aim of this study was to perform a systematic review of diagnostic test accuracy of the criteria used to define keratoconus progression. METHODS: A systematic search of MEDLINE/PubMed, EMBASE, Web of Science Core Collection, and Cochrane Central Register of Controlled Trials (CENTRAL) databases was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-An...
PURPOSE: Early onset scoliosis comprises spinal deformities in children younger than 10, creating challenges in diagnosis, risk assessment, and manage...
AIM: To examine the perinatal experiences of at-risk mothers and their engagement with mobile-health-based care. DESIGN: A qualitative descriptive stu...
AIMS: The study focused on nurses' familiarity with, beliefs about, and attitudes towards artificial intelligence, aiming to identify configurations o...
AIM: To systematically map evidence on the application of AI systems in nursing workforce management, with a targeted focus on the role of nurse leade...
OBJECTIVE: Treatment decision-making for non-small cell lung cancer (NSCLC) is complex, necessitating individualized decision-support tools to improve...
Biosensors have become essential analytical tools that integrate biological recognition elements with physical transducers to detect specific analytes...
BACKGROUND: Bronchial asthma is a complex, highly heterogeneous disease involving multiple pathological mechanisms and inflammatory pathways. Traditio...
Hepatoblastoma (HB) is the most common primary malignant liver tumor in children. Although the incidence is low, it is a serious threat to children's ...
AIMS: To predict nurses' turnover intention using machine learning techniques and identify the most influential psychosocial, organisational and demog...
BACKGROUND: Early detection of cancer reduces mortality and morbidity, but conventional screening methods often face challenges such as invasiveness, ...
BACKGROUND AND AIMS: The substantial miss rate during screening and surveillance colonoscopy, particularly for the right side, underscores the need to...
AIM: This study aimed to validate the mediating role of nurses' AI trust in the relationship between AI uncertainties and AI competence. DESIGN: A cro...
OBJECTIVE: Researchers in otolaryngology-head and neck surgery (OHNS) have sought to explore the potential of large language models (LLMs), but many p...
UTIs are regarded as the second most prevalent global problem, with 150 to 250 million cases reported annually. Poor hygiene, anatomical abnormalities...
BACKGROUND: Machine-learning models are increasingly used in orthopaedic oncology to predict survival outcomes for patients with osteosarcoma. Typical...
OBJECTIVES: Artificial intelligence (AI)-assisted breast cancer screening may improve diagnostic accuracy; however, the long-term health outcomes and ...
AIMS: To (1) analyse managers' experiences with handling patient safety incident reports in an incident reporting software, identifying key challenges...
Artificial intelligence (AI) is reshaping infectious disease diagnostics by supporting clinical decision making, optimising laboratory and clinical wo...