AIMC Topic: Triage

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Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms.

The Lancet. Digital health
BACKGROUND: Artificial intelligence (AI) algorithms can be trained to recognise tuberculosis-related abnormalities on chest radiographs. Various AI algorithms are available commercially, yet there is little impartial evidence on how their performance...

Development of a field artificial intelligence triage tool: Confidence in the prediction of shock, transfusion, and definitive surgical therapy in patients with truncal gunshot wounds.

The journal of trauma and acute care surgery
BACKGROUND: In-field triage tools for trauma patients are limited by availability of information, linear risk classification, and a lack of confidence reporting. We therefore set out to develop and test a machine learning algorithm that can overcome ...

Advancing care for acute gastrointestinal bleeding using artificial intelligence.

Journal of gastroenterology and hepatology
The future of gastrointestinal bleeding will include the integration of machine learning algorithms to enhance clinician risk assessment and decision making. Machine learning algorithms have shown promise in outperforming existing clinical risk score...

Decision Support for Tactical Combat Casualty Care Using Machine Learning to Detect Shock.

Military medicine
INTRODUCTION: The emergence of more complex Prolonged Field Care in austere settings and the need to assist inexperienced providers' ability to treat patients create an urgent need for effective tools to support care. We report on a project to develo...

An East Coast Perspective on Artificial Intelligence and Machine Learning: Part 2: Ischemic Stroke Imaging and Triage.

Neuroimaging clinics of North America
Acute ischemic stroke constitutes approximately 85% of strokes. Most strokes occur in community settings; thus, automatic algorithms techniques are attractive for managing these cases. This article reviews the use of deep learning convolutional neura...

Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study.

The Lancet. Digital health
BACKGROUND: We examined the potential change in cancer detection when using an artificial intelligence (AI) cancer-detection software to triage certain screening examinations into a no radiologist work stream, and then after regular radiologist asses...

Limited evidence of benefits of patient operated intelligent primary care triage tools: findings of a literature review.

BMJ health & care informatics
INTRODUCTION: There is consistent evidence that the workload in general practices is substantially increasing. The digitalisation of healthcare including the use of artificial intelligence has been suggested as a solution to this problem. We wanted t...

Machine Learning Models Improve the Diagnostic Yield of Peripheral Blood Flow Cytometry.

American journal of clinical pathology
OBJECTIVES: Peripheral blood flow cytometry (PBFC) is useful for evaluating circulating hematologic malignancies (HM) but has limited diagnostic value for screening. We used machine learning to evaluate whether clinical history and CBC/differential p...