Hospital-Based Medicine

Surveillance

Latest AI and machine learning research in surveillance for healthcare professionals.

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Advancement of post-market surveillance of medical devices leveraging artificial intelligence: Patient monitors case study.

BackgroundHealthcare institutions throughout the world rely on medical devices to provide their serv...

Advancement of post-market surveillance of medical devices leveraging artificial intelligence: Infusion pumps case study.

BackgroundAnalysis of data from incident registries such as MAUDE has identified the need to improve...

Predicting Epidural Hematoma Expansion in Traumatic Brain Injury: A Machine Learning Approach.

IntroductionTraumatic brain injury (TBI) is a leading cause of disability and mortality worldwide, w...

Artificial intelligence and microbiome research: Evolution of hotspots, research trends, and thematic-based narrative review.

Artificial intelligence (AI) and microbiome have emerged in recent years as transformative fields wi...

Artificial intelligence-driven intelligent learning models for identification and prediction of cardioneurological disorders: A comprehensive study.

The integration of Artificial Intelligence (AI) and Intelligent Learning Models (ILMs) in healthcare...

Machine learning-based prediction model for brain metastasis in patients with extensive-stage small cell lung cancer.

Brain metastases (BMs) in extensive-stage small cell lung cancer (ES-SCLC) are often associated with...

Machine learning estimates on the impacts of detection times on wildfire suppression costs.

As climate warming exacerbates wildfire risks, prompt wildfire detection is an essential step in des...

Advancing clinical MRI exams with artificial intelligence: Japan's contributions and future prospects.

In this narrative review, we review the applications of artificial intelligence (AI) into clinical m...

Neural network-based predictions of antimicrobial resistance phenotypes in multidrug-resistant from whole genome sequencing and gene expression.

Whole genome sequencing (WGS) potentially represents a rapid approach for antimicrobial resistance g...

Cultivating diagnostic clarity: The importance of reporting artificial intelligence confidence levels in radiologic diagnoses.

Accurate image interpretation is essential in the field of radiology to the healthcare team in order...

Machine learning in causal inference for epidemiology.

In causal inference, parametric models are usually employed to address causal questions estimating t...

MultiADE: A Multi-domain benchmark for Adverse Drug Event extraction.

OBJECTIVE: Active adverse event surveillance monitors Adverse Drug Events (ADE) from different data ...

Impact of different nephrectomy types on M0 renal cell carcinoma outcomes in a propensity score matching and deep learning study.

There are few analyses comparing complete nephrectomy with resection of the renal parenchyma only (C...

ChatGPT and radiology report: potential applications and limitations.

Large language models like ChatGPT, with their growing accessibility, are attracting increasing inte...

Random survival forest algorithm for risk stratification and survival prediction in gastric neuroendocrine neoplasms.

This study aimed to construct and assess a machine-learning algorithm designed to forecast survival ...

Human-Artificial Intelligence Symbiotic Reporting for Theranostic Cancer Care.

Reporting of diagnostic nuclear images in clinical cancer management is generally qualitative. Thera...

Machine learning models including patient-reported outcome data in oncology: a systematic literature review and analysis of their reporting quality.

PURPOSE: To critically examine the current state of machine learning (ML) models including patient-r...

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