AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

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Evaluation of a machine learning-based metabolic marker for coronary artery disease in the UK Biobank.

Atherosclerosis
BACKGROUND AND AIMS: An in silico quantitative score of coronary artery disease (ISCAD), built using machine learning and clinical data from electronic health records, has been shown to result in gradations of risk of subclinical atherosclerosis, cor...

Machine learning and clinician predictions of antibiotic resistance in Enterobacterales bloodstream infections.

The Journal of infection
BACKGROUND: Patients with Gram-negative bloodstream infections are at risk of serious adverse outcomes without active treatment, but identifying who has antimicrobial resistance (AMR) to target empirical treatment is challenging.

Development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors.

Alzheimer's research & therapy
BACKGROUND: Dementia is a major public health challenge in modern society. Early detection of high-risk dementia patients and timely intervention or treatment are of significant clinical importance. Neural network survival analysis represents the mos...

Automated redaction of names in adverse event reports using transformer-based neural networks.

BMC medical informatics and decision making
BACKGROUND: Automated recognition and redaction of personal identifiers in free text can enable organisations to share data while protecting privacy. This is important in the context of pharmacovigilance since relevant detailed information on the cli...

Clinicians' perspectives on the use of artificial intelligence to triage MRI brain scans.

European journal of radiology
Artificial intelligence (AI) tools can triage radiology scans to streamline the patient pathway and also relieve clinician workload. Validated AI tools can mitigate the delays in reporting scans by flagging time-sensitive and actionable findings. In ...

Machine learning in the prediction of human wellbeing.

Scientific reports
Subjective wellbeing data are increasingly used across the social sciences. Yet, despite the widespread use of such data, the predictive power of approaches commonly used to model wellbeing is only limited. In response, we here use tree-based Machine...

Evaluating the pathological and clinical implications of errors made by an artificial intelligence colon biopsy screening tool.

BMJ open gastroenterology
OBJECTIVE: Artificial intelligence (AI) tools for histological diagnosis offer great potential to healthcare, yet failure to understand their clinical context is delaying adoption. IGUANA (Interpretable Gland-Graphs using a Neural Aggregator) is an A...

Deep learning and genome-wide association meta-analyses of bone marrow adiposity in the UK Biobank.

Nature communications
Bone marrow adipose tissue is a distinct adipose subtype comprising more than 10% of fat mass in healthy humans. However, the functions and pathophysiological correlates of this tissue are unclear, and its genetic determinants remain unknown. Here, w...

Nostalgia encourages exploration and fosters uncertainty in response to AI technology.

The British journal of social psychology
The burgeoning progress of cutting-edge technology paradoxically evokes nostalgia. How does this emotion influence responses to innovative technology, such as Artificial Intelligence (AI)? We hypothesized that two pathways operate concurrently. First...

Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study.

European heart journal
BACKGROUND AND AIMS: Current heart failure (HF) risk stratification strategies require comprehensive clinical evaluation. In this study, artificial intelligence (AI) applied to electrocardiogram (ECG) images was examined as a strategy to predict HF r...