Latest AI and machine learning research in surveys for healthcare professionals.
The estimates of national disease risk are considerably limited by the time of conducted surveys and the geographical inadequacies in surveillance, notwithstanding malaria’s continued prominence in morbidity and mortality in Nigeria. There is limited research employing machine learning to integrate long term environmental trends with DHS/MIS biomarker data on a national scale, despite the establis...
Neoadjuvant chemotherapy (NAC) is the standard of care for locally advanced breast cancer. However, the disconnect between efficacy in randomized trials and effectiveness in real-world practice—attributable to real-world treatment delays and adherence barriers—remains underexplored for early-stage (cT1-cT3) operable disease. We applied the Target Trial Emulation (TTE) framework to a propensity-sco...
Depression is a leading cause of global disability. Timely identification of patients at risk for clinical worsening remains a major challenge. Electr...
Drug-drug interactions (DDIs) are a significant source of morbidity and adverse drug events (ADEs), particularly in situations of polypharmacy and com...
Large language models are increasingly used for clinical decision support yet may perpetuate socioeconomic biases. Whether simple prompt-based interve...
This study aimed to systematically review and critically evaluate the risk of bias and applicability of surgical site infection (SSI) risk prediction ...
The dispersed node locations and complex topologies of edge networks, combined with intricate dynamic microservice dependencies, render traditional ...
Studies have indicated that personality is related to achievement, and several personality assessment models have been developed. However, most are ...
Chronic urticaria, characterised by pruritic wheals, angioedema or both significantly impacts individuals' quality of life. This review article examin...
An understanding of the nature and function of human trust in artificial intelligence (AI) is fundamental to the safe and effective integration of the...
Digital health technologies are being increasingly integrated into mental healthcare. This means that patients have different treatment options, and c...
BACKGROUND: Artificial intelligence (AI) methods have established themselves in cardiovascular magnetic resonance (CMR) as automated quantification to...
Effective evaluation and governance of predictive models used in health care, particularly those driven by artificial intelligence (AI) and machine le...
Life support robots in care settings must be able to read a person's emotions from facial expressions to achieve empathic communication. This study ai...
Political bias is an inescapable characteristic in news and media reporting, and understanding what political biases people are exposed to when intera...
The rapid development and integration of interconnected healthcare devices and communication networks within the Internet of Medical Things (IoMT) hav...
Artificial intelligence (AI) systems are increasingly being integrated in clinical care, including for AI-powered note-writing. We aimed to develop an...
The purpose of this study was to examine the reliability and agreement between human raters (novice, intermediate, and expert) and TuMeke Risk Suite w...
Introduction: Healthcare AI models often inherit biases from their training data. While efforts have primarily targeted bias in structured data, men...
The rapid development of large language models (LLMs) and large vision models (LVMs) have propelled the evolution of multi-modal AI systems, which h...