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Trust transfer from medical AI to doctors and hospitals: Integrating digital, AI, and scientific literacy in a cross-sectional framework.

BMC medical ethics
This study investigates how different forms of literacy shape trust in medical AI and its transfer in healthcare contexts. Based on a survey of 1,250 participants, three findings emerge. First, digital literacy and AI literacy exert opposite influenc...

Construction and validation of a multi-dimensional health indicator-driven osteoporosis risk prediction model: a large-sample cross-sectional study based on two centers.

BMC musculoskeletal disorders
BACKGROUND: Rising osteoporosis prevalence among elderly populations and limitations of current single-factor screening methods necessitate development of comprehensive multi-dimensional risk prediction models.

Prediction of suicidal ideation and depression in the general population with subthreshold insomnia using machine learning models.

BMC psychiatry
BACKGROUND: Insomnia is a significant independent risk factor for depression and suicidality. However, these conditions often go undetected, particularly in individuals presenting with sleep complaints. This study aimed to develop and validate machin...

Association between lipid profiles and early clinical outcomes in acute ischemic stroke: a single-center cohort study in the Chinese population.

BMC neurology
BACKGROUND: The clinical significance and contribution of the lipid profile in atherosclerosis are well established. However, further investigation is needed in stroke patients, particularly regarding apolipoprotein B100 (ApoB100), a novel non-tradit...

Multicenter study of CT-based deep learning for predicting preoperative T staging and TNM staging in clear cell renal cell carcinoma.

BMC cancer
BACKGROUND: Accurate preoperative T and TNM staging of clear cell renal cell carcinoma (ccRCC) is crucial for diagnosis and treatment, but these assessments often depend on subjective radiologist judgment, leading to interobserver variability. This s...

Machine learning for sudden cardiac death prediction among older adults using community-based electronic health records.

BMC public health
BACKGROUND: Machine learning (ML) models have shown good performance in predicting cardiovascular disease risk. However, the usefulness of ML models has yet to be fully elucidated for sudden cardiac death (SCD) risk using long-term follow-up electron...

The influence of human agency beliefs on ascribing gaze-signalled communicative intent.

Scientific reports
Communication with artificial agents, such as virtual characters and social robots, is becoming more prevalent, making it crucial to understand how their behaviours can best support social interaction. Eye gaze is a key communicative behaviour, as it...

Dynamic HGI trajectories and their impact on survival in patients with sepsis: a machine learning prognostic model.

Inflammation research : official journal of the European Histamine Research Society ... [et al.]
BACKGROUND: Previous studies have indicated a correlation between the glycosylated hemoglobin index (HGI) and the prognosis of patients with sepsis. However, the impact of its dynamic fluctuations on patient outcomes remains insufficiently explored. ...

Early subtypes and progressions of progressive supranuclear palsy: a data-driven brain bank study.

Journal of neurology
BACKGROUND: Progressive supranuclear palsy (PSP) is typically characterized by vertical supranuclear gaze palsy and early falls, referred to as Richardson's syndrome (PSP-RS). Other presentations include postural instability (PSP-PI), Parkinsonism (P...

Bio-inspired neutrosophic-enzyme intelligence framework for pediatric dental disease detection using multi-modal clinical data.

Scientific reports
Pediatric oral diseases affect over 60% of children globally, yet current diagnostic approaches lack precision and speed necessary for early intervention. This study developed a novel bio-inspired neutrosophic-enzyme intelligence framework integratin...