Evaluating Large Language Model-Assisted Emergency Triage: A Comparison of Acuity Assessments by GPT-4 and Medical Experts.

Aim: To evaluate the accuracy of the Emergency Severity Index (ESI) assignments by GPT-4, a large language model (LLM), compared to senior emergency department (ED) nurses and physicians.

Method: An observational study of 100 consecutive adult ED patients was conducted. ESI scores assigned by GPT-4, triage nurses, and by a senior clinician.

Evaluating ten years of breast cancer screening with contrast enhanced mammography in women with Intermediate-high risk.

Purpose: While mammography is considered the gold standard for screening women for breast cancer, its accuracy declines in women with dense breasts. The purpose of the study is to evaluate the diagnostic accuracy of contrast enhanced mammography (CEM) for detecting breast cancer in intermediate and high-risk women, including those with genetic predispositions, over a decade-long cohort at a tertiary center.

Methods: We retrospectively analyzed all CEM examinations performed for screening purposes at a tertiary center between 2012 and 2023.

Generative pre-trained transformer (GPT)-4 support for differential diagnosis in neuroradiology.

Background: Differential diagnosis in radiology relies on the accurate identification of imaging patterns. The use of large language models (LLMs) in radiology holds promise, with many potential applications that may enhance the efficiency of radiologists' workflow. The study aimed to evaluate the efficacy of generative pre-trained transformer (GPT)-4, a LLM, in providing differential diagnoses in neuroradiology, comparing its performance with board-certified neuroradiologists.

Assessing GPT-4 multimodal performance in radiological image analysis.

Objectives: This study aims to assess the performance of a multimodal artificial intelligence (AI) model capable of analyzing both images and textual data (GPT-4V), in interpreting radiological images. It focuses on a range of modalities, anatomical regions, and pathologies to explore the potential of zero-shot generative AI in enhancing diagnostic processes in radiology.

Methods: We analyzed 230 anonymized emergency room diagnostic images, consecutively collected over 1 week, using GPT-4V.

Advancing radiology practice and research: harnessing the potential of large language models amidst imperfections.

Large language models (LLMs) are transforming the field of natural language processing (NLP). These models offer opportunities for radiologists to make a meaningful impact in their field. NLP is a part of artificial intelligence (AI) that uses computer algorithms to study and understand text data.

Artificial intelligence for detection of effusion and lipo-hemarthrosis in X-rays and CT of the knee.

Background: Traumatic knee injuries are challenging to diagnose accurately through radiography and to a lesser extent, through CT, with fractures sometimes overlooked. Ancillary signs like joint effusion or lipo-hemarthrosis are indicative of fractures, suggesting the need for further imaging. Artificial Intelligence (AI) can automate image analysis, improving diagnostic accuracy and help prioritizing clinically important X-ray or CT studies.

Artificial Intelligence for Identification of Images with Active Bleeding in Mesenteric and Celiac Arteries Angiography.

Purpose: The purpose of this study is to evaluate the efficacy of an artificial intelligence (AI) model designed to identify active bleeding in digital subtraction angiography images for upper gastrointestinal bleeding.

Methods: Angiographic images were retrospectively collected from mesenteric and celiac artery embolization procedures performed between 2018 and 2022. This dataset included images showing both active bleeding and non-bleeding phases from the same patients.

Utilizing large language models in breast cancer management: systematic review.

Purpose: Despite advanced technologies in breast cancer management, challenges remain in efficiently interpreting vast clinical data for patient-specific insights. We reviewed the literature on how large language models (LLMs) such as ChatGPT might offer solutions in this field.

Methods: We searched MEDLINE for relevant studies published before December 22, 2023.

AI in the ED: Assessing the efficacy of GPT models vs. physicians in medical score calculation.

Background And Aims: Artificial Intelligence (AI) models like GPT-3.5 and GPT-4 have shown promise across various domains but remain underexplored in healthcare. Emergency Departments (ED) rely on established scoring systems, such as NIHSS and HEART score, to guide clinical decision-making.

Advancing Medical Practice with Artificial Intelligence: ChatGPT in Healthcare.

Background: Advancements in artificial intelligence (AI) and natural language processing (NLP) have led to the development of language models such as ChatGPT. These models have the potential to transform healthcare and medical research. However, understanding their applications and limitations is essential.

Findings on emergent magnetic resonance imaging in pregnant patients with suspected appendicitis: A single center perspective.

This study's aim is to describe the imaging findings in pregnant patients undergoing emergent MRI for suspected acute appendicitis, and the various alternative diagnoses seen on those MRI scans. This is a single center retrospective analysis in which we assessed the imaging, clinical and pathological data for all consecutive pregnant patients who underwent emergent MRI for suspected acute appendicitis between April 2013 and June 2021. Out of 167 patients, 35 patients (20.

Assessment of glymphatic dysfunction in pediatric idiopathic intracranial hypertension: insights from quantitative diffusivity and perivascular spaces analysis-a case-control study.

Background: The impaired drainage of cerebrospinal fluid through the glymphatic system is thought to play a role in the idiopathic intracranial hypertension (IIH) pathophysiology. Limited data exist regarding the glymphatic system's involvement in pediatric patients with IIH. Therefore, the study's objective was to quantitatively evaluate alterations in parenchymal diffusivity and magnetic resonance imaging (MRI)-visible dilated perivascular spaces (PVS) as imaging indicators of glymphatic dysfunction in pediatric patients with IIH.

Performance of BeBack Crossing Catheter in Chronic Total Occlusion in Patients With Chronic Limb-Threatening Ischemia: A Single-Center Experience.

Purpose: To describe a single-center experience in the treatment of chronic limb-threatening ischemia (CLTI) with the application of BeBack catheter (Bentley InnoMed, Germany) in patients with arterial chronic total occlusion (CTO).

Materials And Methods: A retrospective review of patients who underwent limb revascularizations using the BeBack catheter between 2015 and 2022. All patients had an initial failed attempt using a traditional guidewire and catheter technique.

Adversarial attacks in radiology - A systematic review.

Purpose: The growing application of deep learning in radiology has raised concerns about cybersecurity, particularly in relation to adversarial attacks. This study aims to systematically review the literature on adversarial attacks in radiology.

Methods: We searched for studies on adversarial attacks in radiology published up to April 2023, using MEDLINE and Google Scholar databases.

Towards AI-Augmented Clinical Decision-Making: An Examination of ChatGPT's Utility in Acute Ulcerative Colitis Presentations.

This study explores the potential of OpenAI's ChatGPT as a decision support tool for acute ulcerative colitis presentations in the setting of an emergency department. We assessed ChatGPT's performance in determining disease severity using TrueLove and Witts criteria and the necessity of hospitalization for patients with ulcerative colitis, comparing results with those of expert gastroenterologists. Of 20 cases, ChatGPT's assessments were found to be 80% consistent with gastroenterologist evaluations and indicated a high degree of reliability.

Retrograde tibial access for revascularization of chronic total occlusions in patients with chronic limb-threatening ischemia.

Purpose: To evaluate tibial single access in treatment of chronic total occlusions (CTO) in patients with ipsilateral chronic-limb ischemia (CLTI).

Materials And Methods: In this retrospective study, data was collected on patients treated for ipsilateral CTO via a tibial artery access between March 2017 and March 2021. Fifty-nine limbs in 57 patients, (42 men, average age 73 years; range 47-96) were treated.

Effect of magnet strength on fetal brain biometry - a single-center retrospective MRI-based cohort study.

Purpose: Abnormal fetal brain measurements might affect clinical management and parental counseling. The effect of between-field-strength differences was not evaluated in quantitative fetal brain imaging until now. Our study aimed to compare fetal brain biometry measurements in 3.

ChatGPT-4 Assistance in Optimizing Emergency Department Radiology Referrals and Imaging Selection.

Purpose: The quality of radiology referrals influences patient management and imaging interpretation by radiologists. The aim of this study was to evaluate ChatGPT-4 as a decision support tool for selecting imaging examinations and generating radiology referrals in the emergency department (ED).

Methods: Five consecutive clinical notes from the ED were retrospectively extracted, for each of the following pathologies: pulmonary embolism, obstructing kidney stones, acute appendicitis, diverticulitis, small bowel obstruction, acute cholecystitis, acute hip fracture, and testicular torsion.

Large language model (ChatGPT) as a support tool for breast tumor board.

Large language models (LLM) such as ChatGPT have gained public and scientific attention. The aim of this study is to evaluate ChatGPT as a support tool for breast tumor board decisions making. We inserted into ChatGPT-3.

Multimodal fusion models for pulmonary embolism mortality prediction.

Pulmonary embolism (PE) is a common, life threatening cardiovascular emergency. Risk stratification is one of the core principles of acute PE management and determines the choice of diagnostic and therapeutic strategies. In routine clinical practice, clinicians rely on the patient's electronic health record (EHR) to provide a context for their medical imaging interpretation.

Large language models for oncological applications.

Large language models such as ChatGPT have gained public and scientific attention. These models may support oncologists in their work. Oncologists should be familiar with large language models to harness their potential while being aware of potential dangers and limitations.