AIMC Topic: Triage

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MIASurviveMTP: Machine learning for immediate assessment and survival prediction after massive transfusion protocol.

PloS one
Early triage of trauma patients requiring massive transfusion (MT) may help to marshal appropriate resources and improve treatment and outcome. Artificial intelligence (AI) and machine learning (ML) offer theoretical advantages compared to convention...

AI in primary care - a general practitioner's bucket list.

The European journal of general practice
While the development and use of Artificial Intelligence (AI) in health care have literally exploded in recent years, general practitioners (GPs) continue to struggle with a fragmented health care system and complex patients with multiple conditions ...

Use of a Preliminary Artificial Intelligence-Based Laryngeal Cancer Screening Framework for Low-Resource Settings: Development and Validation Study.

JMIR formative research
BACKGROUND: Early-stage diagnosis of laryngeal cancer significantly improves patient survival and quality of life. However, the scarcity of specialists in low-resource settings hinders the timely review of flexible nasopharyngoscopy (FNS) videos, whi...

Intracellular lymphocyte protein biomarkers for early radiological triage in the human population.

PloS one
In the event of a large-scale radiological or nuclear emergency, a rapid, high-throughput screening tool will be essential for efficient triage of potentially exposed individuals, optimizing scarce medical resources and ensuring timely care. The obje...

Medical triage as an AI ethics benchmark.

Scientific reports
We present the TRIAGE benchmark, a novel machine ethics benchmark designed to evaluate the ethical decision-making abilities of large language models (LLMs) in mass casualty scenarios. TRIAGE uses medical dilemmas created by healthcare professionals ...

A comparison of quality and readability of Artificial Intelligence chatbots in triage for head and neck cancer.

American journal of otolaryngology
OBJECTIVE: Head and neck cancers (HNCs) are a significant global health concern, contributing to substantial morbidity and mortality. AI-powered chatbots such as ChatGPT, Google Gemini, Microsoft Copilot, and Open Evidence are increasingly used by pa...

Predicting Emergency Severity Index (ESI) level, hospital admission, and admitting ward in an emergency department using data-driven machine learning.

BMC medical informatics and decision making
INTRODUCTION: Emergency departments (EDs) are critical for ensuring timely patient care, especially in triage, where accurate prioritisation is essential for patient safety and resource utilisation. Building on previous research, this study leverages...

Predicting patient risk of leaving without being seen using machine learning: a retrospective study in a single overcrowded emergency department.

BMC emergency medicine
Emergency department (ED) overcrowding has become a critical issue in hospital management, leading to increased patient wait times and higher rates of individuals leaving without being seen (LWBS). This study aims to identify key factors influencing ...

Assessing the Accuracy of ChatGPT in Appropriately Triaging Common Postoperative Concerns Regarding Mohs Micrographic Surgery.

JMIR dermatology
Artificial intelligence (AI) is increasingly integrated into health care, offering potential benefits in patient education, triage, and administrative efficiency. This study evaluates AI-driven dialogue interfaces within an electronic health record a...

Machine learning to improve predictive performance of prehospital early warning scores.

Scientific reports
Early warning scores are used to assess acute patients' risk of being in a critical situation, allowing for early appropriate treatment, avoiding critical outcomes. The early warning scores use changes in vital signs to provide an assessment, however...