Latest AI and machine learning research in health policy for healthcare professionals.
The increasing complexity of healthcare systems management requires the development of advanced methodologies to support efficient resource allocation, service delivery, and strategic planning. Artificial intelligence has emerged as an important tool in this domain, offering capabilities to model demand, predict capacity requirements, and inform operational decisions through data-driven insights. ...
BACKGROUND: Embodied intelligence-artificial intelligence instantiated in physical or virtual bodies that can perceive, communicate, and interact with users and their environments-has been increasingly applied in health care. However, the evidence base remains fragmented because of inconsistent terminology, diverse embodiment forms, and limited synthesis of application domains, target populations,...
BACKGROUND: University students experience elevated psychological distress, with limited access to mental health services. While cognitive behavioral ...
Poor pregnancy sleep quality has been linked to adverse pregnancy and birth outcomes, particularly in late pregnancy when sleep symptoms worsen. We ai...
OBJECTIVES: Despite the significant potential for Clinical Decision Support Systems (CDSSs) to improve care processes and health outcomes, several bar...
BACKGROUND: Trustworthy artificial intelligence (AI) in health care requires assurance frameworks that translate ethical principles into measurable go...
PURPOSE: Artificial intelligence (AI)-based text messaging, or "chat," in post-appendectomy care has been shown to decrease preventable emergency depa...
Artificial intelligence and machine learning are increasingly shaping the future of small animal veterinary medicine, particularly through predictive ...
PURPOSE OF REVIEW: This review aims to summarize recent literature on artificial intelligence (AI) tools for adolescent mental health, including the t...
BACKGROUND: Bipolar disorder is a clinically sensitive and diagnostically complex condition in which unclear or incomplete psychoeducational informati...
Debriefing is widely recognised as a central mechanism for learning within healthcare simulation, enabling learners to reflect on clinical actions, de...
Background. Simulation calibration is the process of configuring a simulation model's parameters to improve the agreement between the model output and...
The application of machine learning (ML) models in healthcare management offers high potential. In particular, resource allocation and operational dec...
BACKGROUND: Chronic dermatologic conditions such as psoriasis, atopic dermatitis, and hidradenitis suppurativa are associated with a high burden of ps...
BACKGROUND: Open defecation is the disposal of human feces in fields, bushes, forests, open waterways, beaches, and other open areas. It worsens the e...
The global health workforce is approaching a breaking point, driven by administrative overload, inefficient workflows, burnout, and accelerating retir...
We present the design and implementation of a data curation framework to generate a large-scale clinical brain imaging dataset suitable for artificial...
BACKGROUND: Early detection of sepsis in pediatric intensive care units (PICUs) is critical, but challenging due to its nonspecific clinical presentat...
Despite strong evidence supporting the use of trauma-focused evidence-based psychotherapies (EBPs) to treat posttraumatic stress disorder (PTSD), heal...
OBJECTIVES: To develop a deep learning model capable of automatically detecting common positioning errors in panoramic radiographs and to establish an...