BACKGROUND: Generative artificial intelligence (GenAI) leverages large language models (LLMs) that are transforming health care. Specialized ambient GenAI tools, like Nuance Dax, Speke, and Tandem Health, "listen" to consultations and generate clinic...
The field of AI carries inherent risks such as algorithmic biases, security vulnerabilities, and ethical concerns related to privacy and data protection. Despite these risks, AI holds significant promise for social good, with applications ranging fro...
INTRODUCTION: Although artificial intelligence (AI) has been widely applied to electronic health record (EHR) data in hospital environments, its use in long-term care (LTC) facilities remains unexplored. Limited information technology infrastructure ...
BACKGROUND: Social care systems worldwide face increasing demographic and financial pressures. This necessitates exploring innovative technological solutions to enhance service delivery without substantially increasing costs. Conversational interface...
Evidence-based Practice (EBP) is a vital principle, with its origins in the 1970s, that has transformed the disciplines of medicine and healthcare. The use of best available evidence to inform decisions and best practice has since spread across other...
Rationale Integrating artificial intelligence (AI) into education has introduced transformative possibilities, particularly through adaptive learning systems. Rehabilitation science education stands to benefit significantly from the integration of AI...
BACKGROUND: Social robots (SR), sensorimotor machines designed to interact with humans, can help to respond to the increasing demands in the health care sector. To ensure the successful use of this technology, acceptance is paramount. Generative arti...
BACKGROUND: Randomised controlled trials (RCTs) commonly estimate intention-to-treat (ITT) estimands. However, when nonadherence to assigned treatment occurs, ITT estimands reflect the effect of being offered treatment, rather than adhering to it and...
INTRODUCTION: Methods to adopt artificial intelligence (AI) in healthcare clinical practice remain unclear. The potential for rapid integration of AI-enabled technologies across healthcare settings coupled with the growing digital divide in the healt...
BACKGROUND: The use of artificial intelligence (AI) technologies in radiography practice is increasing. As this advanced technology becomes more embedded in radiography systems and clinical practice, the role of radiographers will evolve. In the cont...
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