AIMC Topic: Triage

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Prospective Evaluation of AI Triage of Pulmonary Emboli on CT Pulmonary Angiograms.

Radiology
Background Artificial intelligence (AI) algorithms have shown high accuracy for detection of pulmonary embolism (PE) on CT pulmonary angiography (CTPA) studies in academic studies. Purpose To determine whether use of an AI triage system to detect PE ...

Intelligent decision support in medical triage: are people robust to biased advice?

Journal of public health (Oxford, England)
BACKGROUND: Intelligent artificial agents ('agents') have emerged in various domains of human society (healthcare, legal, social). Since using intelligent agents can lead to biases, a common proposed solution is to keep the human in the loop. Will th...

Artificial intelligence may enhance emergency triage and management.

The journal of trauma and acute care surgery
The entry of Artificial Intelligence (AI) into Intensive Care has become a reality. We think that only with robust validation studies accomplished by a multidisciplinary team the gap between clinical research and clinical practice can be bridged.

Machine learning models predicting undertriage in telephone triage.

Annals of medicine
BACKGROUND: Undertriaged patients have worse outcomes than appropriately triaged patients. Machine learning provides better triage prediction than conventional triage in emergency departments, but no machine learning-based undertriage prediction mode...

Multicenter, Multivendor Validation of an FDA-approved Algorithm for Mammography Triage.

Journal of breast imaging
OBJECTIVE: Artificial intelligence (AI)-based triage algorithms may improve cancer detection and expedite radiologist workflow. To this end, the performance of a commercial AI-based triage algorithm on screening mammograms was evaluated across breast...

Artificial Intelligence-Based Identification of Normal Chest Radiographs: A Simulation Study in a Multicenter Health Screening Cohort.

Korean journal of radiology
OBJECTIVE: This study aimed to investigate the feasibility of using artificial intelligence (AI) to identify normal chest radiography (CXR) from the worklist of radiologists in a health-screening environment.

Chest X-ray Analysis With Deep Learning-Based Software as a Triage Test for Pulmonary Tuberculosis: An Individual Patient Data Meta-Analysis of Diagnostic Accuracy.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
BACKGROUND: Automated radiologic analysis using computer-aided detection software (CAD) could facilitate chest X-ray (CXR) use in tuberculosis diagnosis. There is little to no evidence on the accuracy of commercially available deep learning-based CAD...

Accuracy of the Traditional COVID-19 Phone Triaging System and Phone Triage-Driven Deep Learning Model.

Journal of primary care & community health
OBJECTIVES: During the COVID-19 pandemic, a quick and reliable phone-triage system is critical for early care and efficient distribution of hospital resources. The study aimed to assess the accuracy of the traditional phone-triage system and phone tr...

Deep Learning Algorithm to Predict Need for Critical Care in Pediatric Emergency Departments.

Pediatric emergency care
BACKGROUND AND OBJECTIVES: Emergency department (ED) overcrowding is a national crisis in which pediatric patients are often prioritized at lower levels. Because the prediction of prognosis for pediatric patients is important but difficult, we develo...

Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms: A Scoping Review.

JAMA dermatology
IMPORTANCE: Clinical artificial intelligence (AI) algorithms have the potential to improve clinical care, but fair, generalizable algorithms depend on the clinical data on which they are trained and tested.