Latest AI and machine learning research in cultural competence for healthcare professionals.
BACKGROUND: Advanced HIV disease remains a major global health concern, with nearly 40.8 million people living with HIV as of 2024. Antiretroviral therapy has improved outcomes, but its success depends on timely intervention, adherence, and retention in care. AI, including machine learning and deep learning, offers promising tools for prognostic modeling that could support clinical decision-making...
BACKGROUND: AI is increasingly encountered in clinical care and medical education, but medical students' attitudes, perceptions, and self-reported familiarity have been assessed using heterogeneous survey instruments, AI referents, and response scales. Prior reviews often combined mixed health profession populations or summarized central estimates without fully showing variation across settings. O...
Artificial intelligence (AI) is increasingly being integrated into oncology for applications including cancer detection, risk stratification, treatmen...
BACKGROUND: Artificial intelligence (AI) has the potential to improve echocardiography. However, AI systems may exhibit sex bias, reproducing societal...
OBJECTIVES: To systematically identify, appraise and synthesise artificial intelligence (AI) and machine-learning (ML) models that predict treatment r...
BACKGROUND: Tuberculosis (TB) remains a leading cause of infectious disease mortality worldwide, and treatment failure contributes to ongoing transmis...
BACKGROUND: Deep learning reconstruction can shorten breath-hold MRI for liver proton density fat fraction (DL-PDFF), but agreement with conventional ...
MOTIVATION: Protein conformation generation remains a fundamental challenge in structural biology and machine learning. Proteins are highly dynamic ma...
WEE1 kinase, a critical regulator of the G2/M checkpoint, represents a validated therapeutic target in tumors harboring defects in DNA damage response...
BACKGROUND: AI-driven clinical systems can improve diagnosis, prognosis, and resource allocation, but they may reproduce disparities encoded in histor...
AI is entering clinical practice faster than health professions curricula can teach it, leaving many educators eager to use AI-based teaching tools bu...
Pediatric bipolar disorder is challenging to diagnose accurately due to symptom heterogeneity. More standardized and data-driven approaches are needed...
BACKGROUND: AI has the potential to transform health care in low- and middle-income countries, where access to quality care remains limited. Maternal,...
BACKGROUND: Bipolar disorder (BD) features episodic shifts among mania, hypomania, depression, mixed states, and euthymia. Timely detection of mood tr...
Advances in digital health have dramatically changed how patients engage with their health. Rather than relying solely on periodic clinical visits, pa...
BACKGROUND: Accurately predicting operative difficulty in laparoscopic cholecystectomy (LC) is foundational to personalized surgical planning and pati...
Machine learning (ML) systems for medical imaging have demonstrated remarkable diagnostic capabilities. However, their susceptibility to learning spur...
AI is being rapidly integrated across nearly all sectors, including within healthcare and health systems. The prevailing dominant narrative frames AI'...
Facial recognition (FR) models are vulnerable to adversarial attacks, in which attackers manipulate facial images to expose system vulnerabilities, un...
This study investigates how architectural design influences the behavior of lightweight convolutional neural networks (CNNs) in multilabel classificat...