Latest AI and machine learning research in information technology for healthcare professionals.
The last three years have seen an explosion in published manuscripts analysing open-access health datasets, in many cases presenting misleading or biologically implausible findings. There is a growing evidence base to suggest that this is due in part to AI-assisted and formulaic workflows. Here we employ a top-down scientometric analysis to investigate which datasets have seen publication rates de...
Multimorbidity poses significant healthcare challenges globally. Current assessment methods rely primarily on structured electronic health record (EHR) data, potentially missing valuable information contained in unstructured clinical notes. Natural language processing (NLP) techniques offer promising solutions for extracting comprehensive multimorbidity data from these unstructured sources. To ide...
Fifth metatarsal (5MT) fractures are common but challenging to diagnose, particularly with limited expertise or subtle fractures. Deep learning shows ...
Skin cancer, one of the most prevalent forms of cancer globally, demands early and accurate diagnosis to improve patient outcomes. In this paper, we p...
Diagnosis coding is essential for clinical care, research validity, and hospital reimbursement. In neonatal settings, manual coding is frequently erro...
Eviction is a significant yet understudied social determinants of health (SDoH), linked to housing instability, unemployment, and mental health. While...
Poor outcomes in acute respiratory distress syndrome (ARDS) can be alleviated with tools that support early diagnosis. Current machine learning method...
To streamline the development of clinical machine learning (ML) models for predicting acute neurological morbidity in critically ill children by exten...
Most evaluations of artificial intelligence (AI) in medicine rely on static, multiple-choice benchmarks that fail to capture the dynamic, sequential n...
Postpartum hemorrhage (PPH) is a major cause of maternal morbidity and mortality. Timely prediction may prevent adverse maternal outcomes, and efforts...
To quantify the adoption pattern of an LLM-based clinical decision support system across private primary health facilities in Kenya (operated by Penda...
The dire consequences of heart failure (HF) patient non-response to guideline directed medical therapy often fuel early, non-selective referral for su...
Ambient artificial intelligence (AI) offers the potential to reduce documentation burden and improve efficiency through clinical note generation. Wide...
The accurate detection of clinical phenotypes from electronic health records (EHRs) is pivotal for advancing large-scale genetic and longitudinal stud...
Alzheimer’s Disease (AD) is a complex neurodegenerative disorder strongly influenced by sex differences, with women comprising nearly two-thirds of ca...
Diabetes-related foot ulcers (DFUs) are a serious complication of diabetes, often resulting in infection, amputation, or even mortality. Offloading fo...
Obsessive-compulsive disorder (OCD) is a common psychiatric disorder, with two-thirds of affected individuals reporting severe impairment. Despite its...
Artificial intelligence (AI)-enhanced electrocardiogram (ECG) models are designed to detect specific anatomical and functional cardiac abnormalities. ...
By personalizing healthcare to an individual’s specific requirements, precision health promises to maximize benefit and minimize harm, thereby maximiz...
The temporal sequence of clinical events is crucial in outcomes research, yet standard machine learning (ML) approaches often overlook this aspect in ...