Managing rheumatic diseases requires teamwork, but referral patterns and challenges remain poorly understood. This study explored rheumatologists' perspectives on referral patterns in the Gulf countries. We conducted a web-based, 21-question cross-se...
Purpose To develop and evaluate machine learning and deep learning-based models for automated protocoling of emergency brain MRI scans based on clinical referral text. Materials and Methods In this single-institution, retrospective study of 1953 emer...
BACKGROUND: Generative artificial intelligence (GenAI) has the potential to revolutionise healthcare delivery. The nuances of real-life clinical practice and complex clinical environments demand a rigorous, evidence-based approach to ensure safe and ...
Artificial intelligence (AI) has shown promise in revolutionizing medical triage, particularly in the context of the rising prevalence of kidney-related conditions with the aging global population. This study evaluates the utility of ChatGPT, a large...
OBJECTIVE: To evaluate the accuracy of Google Translate (GT) in translating low-acuity paediatric emergency consultations involving respiratory symptoms and fever, and to examine legal and policy implications of using AI-based language interpretation...
: Diabetes is a rapidly increasing global health challenge compounded by a critical shortage of diabetes care and education specialists. Robot-assisted diabetes care offers a cost-effective and scalable alternative to traditional methods such as trai...
BACKGROUND AND OBJECTIVE: This study evaluated optometrists' referral patterns for epiretinal membrane (ERM) patients in Ontario, Canada, and their attitudes towards an artificial intelligence (AI) tool for improving referral accuracy. An anonymous o...
INTRODUCTION: ChatGPT, a widely accessible AI program, has demonstrated potential in various healthcare applications, including emergency department (ED) triage, differential diagnosis, and patient education. However, its potential in providing recom...
BMC medical informatics and decision making
40055634
BACKGROUND: Clinical decision-making in healthcare often relies on unstructured text data, which can be challenging to analyze using traditional methods. Natural Language Processing (NLP) has emerged as a promising solution, but its application in cl...
BACKGROUND: The increasing use of artificial intelligence (AI) in medical diagnosis and consultation promises benefits such as greater accuracy and efficiency. However, there is little evidence to systematically test whether the ideal technological p...