AI & Society, states that algorithmic systems often construct competing but equally valid “model-worlds,” offering empirical support for a philosophical claim that evidence alone cannot uniquely ...

A recent study on the development and validation of an AI-based framework for first-trimester preeclampsia risk assessment ...

Researchers sought to determine an effective approach to predict postembolization fever in patients undergoing TACE.

Background and objectives Lung cancer remains the leading cause of cancer-related mortality worldwide. Early detection of pulmonary nodules is crucial for timely diagnosis and effective treatment.

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

Introduction Ischaemic heart disease (IHD) remains a leading cause of morbidity and mortality worldwide and poses an ...

Objectives Since 1985, the international healthcare community has recommended the ideal rate of caesarean section (CS) to be 10%–15% at the national level. The literature has reported that overused CS ...

A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...

Background Approximately one in three patients with acute ischemic stroke (AIS) suffer from a premorbid disability prior to ...

Objectives To describe the incidence, presentation and long-term health outcomes of suicidal thoughts and behaviours (STBs) in children aged 12 years or under. Methods This population-based study ...