Hospital-Based Medicine

The development of an artificial intelligence auto-segmentation tool for 3D volumetric analysis of vestibular schwannomas.

Linear and volumetric analysis are the typical methods to measure tumor size. 3D volumetric analysis has risen in popularity; however, this is very time and labor intensive limiting its implementation in clinical practice. This study aims to show that an AI-led approach can shorten the length of time required to conduct 3D volumetric analysis of VS tumors and improve image processing accuracy. Fro...

Artificial Intelligence in Commercial Industry: Serving the End-to-End Patient Experience Across the Digital Ecosystem.

The purpose of this article is to evaluate the application of artificial intelligence (AI) from the perspective of the orthopaedic industry with respect to the specific opportunities offered by AI. It is clear that AI has the potential to impact the entire continuum of musculoskeletal and orthopaedic care. The following areas may experience improvements from integrating AI into surgical applicatio...

Towards clinical prediction with transparency: An explainable AI approach to survival modelling in residential aged care.

BACKGROUND AND OBJECTIVE: Scalable, flexible and highly interpretable tools for predicting mortality in residential aged care facilities for the purpo...

AI and early diagnostics: mapping fetal facial expressions through development, evolution, and 4D ultrasound.

The development of facial musculature and expressions in the human fetus represents a critical intersection of developmental biology, neurology, and e...

Prediction of 90 day mortality in elderly patients with acute HF from e-health records using artificial intelligence.

AIMS: Mortality risk after hospitalization for heart failure (HF) is high, especially in the first 90 days. This study aimed to construct a model auto...

Development of Evidence-Based Guidelines for the Integration of Generative AI in University Education Through a Multidisciplinary, Consensus-Based Approach.

INTRODUCTION: The introduction highlights the transformative impact of generative artificial intelligence (GenAI) on higher education (HE), emphasisin...

Reliability-enhanced data cleaning in biomedical machine learning using inductive conformal prediction.

Accurately labeling large datasets is important for biomedical machine learning yet challenging while modern data augmentation methods may generate no...

Artificial Intelligence Model for Detection of Colorectal Cancer on Routine Abdominopelvic CT Examinations: A Training and External-Testing Study.

Radiologists are prone to missing some colorectal cancers (CRCs) on routine abdominopelvic CT examinations that are in fact detectable on the images....

Accuracy of Commercial Large Language Model (ChatGPT) to Predict the Diagnosis for Prehospital Patients Suitable for Ambulance Transport Decisions: Diagnostic Accuracy Study.

OBJECTIVES: While ambulance transport decisions guided by artificial intelligence (AI) could be useful, little is known of the accuracy of AI in makin...

Chat GPT vs. Clinical Decision Support Systems in the Analysis of Drug-Drug Interactions.

The current standard method for the analysis of potential drug-drug interactions (pDDIs) is time-consuming and includes the use of multiple clinical d...

Bayesian Optimization-Enhanced Reinforcement learning for Self-adaptive and multi-objective control of wastewater treatment.

Controllers of wastewater treatment plants (WWTPs) often struggle to maintain optimal performance due to dynamic influent characteristics and the need...

A multi-domain feature fusion epilepsy seizure detection method based on spike matching and PLV functional networks.

The identification of spikes, as a typical characteristic wave of epilepsy, is crucial for diagnosing and locating the epileptogenic region. The tradi...

Consecutive prediction of adverse maternal outcomes of preeclampsia, using the PIERS-ML and fullPIERS models: A multicountry prospective observational study.

BACKGROUND: Preeclampsia is a potentially life-threatening pregnancy complication. Among women whose pregnancies are complicated by preeclampsia, the ...

An AI-assisted morphoproteomic approach is a supportive tool in esophagitis-related precision medicine.

Esophagitis is a frequent, but at the molecular level poorly characterized condition with diverse underlying etiologies and treatments. Correct diagno...

ChatGPT-4 Omni's superiority in answering multiple-choice oral radiology questions.

OBJECTIVES: This study evaluates and compares the performance of ChatGPT-3.5, ChatGPT-4 Omni (4o), Google Bard, and Microsoft Copilot in responding to...

Development of a machine learning model and a web application for predicting neurological outcome at hospital discharge in spinal cord injury patients.

BACKGROUND: Spinal cord injury (SCI) is a devastating condition with profound physical, psychological, and socioeconomic consequences. Despite advance...

From errors to excellence: the pre-analytical journey to improved quality in diagnostics. A scoping review.

This scoping review focuses on the evolution of pre-analytical errors (PAEs) in medical laboratories, a critical area with significant implications fo...

Development and Validation of Machine Learning-Based Model for Hospital Length of Stay in Patients Undergoing Endovascular Interventional Embolization for Intracranial Aneurysms.

OBJECTIVE: This study was to explore the factors associated with prolonged hospital length of stay (LOS) in patients with intracranial aneurysms (IAs)...

Evaluating AI performance in nephrology triage and subspecialty referrals.

Artificial intelligence (AI) has shown promise in revolutionizing medical triage, particularly in the context of the rising prevalence of kidney-relat...

Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine).

The COVID-19 pandemic has burdened healthcare systems globally. To curb high hospital admission rates, only patients with genuine medical needs are ad...