Surveillance

Advanced vision transformers and open-set learning for robust mosquito classification: A novel approach to entomological studies.

Mosquito-related diseases pose a significant threat to global public health, necessitating efficient and accurate mosquito classification for effective surveillance and control. This work presents an innovative approach to mosquito classification by leveraging state-of-the-art vision transformers and open-set learning techniques. A novel framework has been introduced that integrates Transformer-ba...

Evaluating generative artificial intelligence's limitations in health policy identification and interpretation.

Policy epidemiology utilizes human subject-matter experts (SMEs) to systematically surface, analyze, and categorize legally-enforceable policies. The Analysis and Mapping of Policies for Emerging Infectious Diseases project systematically collects and assesses health-related policies from all United Nations Member States. The recent proliferation of generative artificial intelligence (GAI) tools p...

QUAIDE - Quality assessment of AI preclinical studies in diagnostic endoscopy.

Artificial intelligence (AI) holds significant potential for enhancing quality of gastrointestinal (GI) endoscopy, but the adoption of AI in clinical ...

AI-enabled clinical decision support tools for mental healthcare: A product review.

The review seeks to promote transparency in the availability of regulated AI-enabled Clinical Decision Support Systems (AI-CDSS) for mental healthcare...

Integrating Sentinel-1 data and machine learning for effective paddy field monitoring in Cauvery Delta Zone, Tamil Nadu, India.

Paddy crop mapping is essential for agricultural monitoring, ensuring food security, and enhancing resource allocation. This study observes the Cauver...

Integrating graph and reinforcement learning for vaccination strategies in complex networks.

Pandemics like COVID-19 have a huge impact on human society and the global economy. Vaccines are effective in the fight against these pandemics but of...

Integrating Interpretability in Machine Learning and Deep Neural Networks: A Novel Approach to Feature Importance and Outlier Detection in COVID-19 Symptomatology and Vaccine Efficacy.

In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural n...

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 surveillance and maintenance strategies for infus...

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 with far-reaching implications for various biomedica...

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 has transformed the field, offering precise diagn...

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 poor survival rates and quality of life, making t...

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 designing an efficient suppression strategy, monitori...

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 magnetic resonance imaging (MRI) exams, with a part...

Applications of artificial intelligence for chemical analysis and monitoring of pharmaceutical and personal care products in water and wastewater: A review.

Specifying and interpreting the occurrence of emerging pollutants is essential for assessing treatment processes and plants, conducting wastewater-bas...

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 genotype-to-phenotype prediction. However, the chal...

Machine learning in causal inference for epidemiology.

In causal inference, parametric models are usually employed to address causal questions estimating the effect of interest. However, parametric models ...

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

OBJECTIVE: Active adverse event surveillance monitors Adverse Drug Events (ADE) from different data sources, such as electronic health records, medica...

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 (CN) or radical nephrectomy that includes simultaneo...

Artificial intelligence algorithms enhance urine cytology reporting confidence in postoperative follow-up for upper urinary tract urothelial carcinoma.

PURPOSE: In Taiwan, the incidence of urothelial carcinoma of the upper urinary tract (UTUC) is high and intravesical recurrence is approximately 22%-4...

ChatGPT and radiology report: potential applications and limitations.

Large language models like ChatGPT, with their growing accessibility, are attracting increasing interest within the artificial intelligence medical fi...