AIMC Topic: World Health Organization

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Adaptive modelling approach for predicting causes of death: insights from verbal autopsy data in Tanzania.

International health
BACKGROUND: The World Health Organization (WHO) has approved the use of a verbal autopsy (VA), a survey-based approach to generate out-of-hospital causes of death (CoDs). Through this study, an adaptive Bayesian networks machine learning model was de...

Large multimodal models: boon or burden for low- and middle-income countries.

International health
Large multimodal models, a type of generative artificial intelligence (AI), could contribute to wider government efforts to achieve universal health coverage if ethical challenges are proactively addressed during the design and deployment of these AI...

Comparative analysis of language models in addressing syphilis-related queries.

Medicina oral, patologia oral y cirugia bucal
BACKGROUND: Syphilis, caused by Treponema pallidum, is a significant global health concern with potentially severe complications if untreated. Advances in artificial intelligence (AI), particularly large language models (LLMs), offer opportunities to...

An epidemiological knowledge graph extracted from the World Health Organization's Disease Outbreak News.

Scientific data
The rapid evolution of artificial intelligence (AI), together with the increased availability of social media and news for epidemiological surveillance, is marking a pivotal moment in epidemiology and public health research. By harnessing the capabil...

Vaccination hesitancy: agreement between WHO and ChatGPT-4.0 or Gemini Advanced.

Annali di igiene : medicina preventiva e di comunita
BACKGROUND: An increasing number of individuals use online Artificial Intelligence (AI) - based chatbots to retrieve information on health-related topics. This study aims to evaluate the accuracy in answering vaccine-related answers of the currently ...

Diffusion-weighted MRI precisely predicts telomerase reverse transcriptase promoter mutation status in World Health Organization grade IV gliomas using a residual convolutional neural network.

The British journal of radiology
OBJECTIVES: Telomerase reverse transcriptase promoter (pTERT) mutation status plays a key role in making decisions and predicting prognoses for patients with World Health Organization (WHO) grade IV glioma. This study was conducted to assess the valu...

Systematic TB screening using WHO radiograph categorisation and care outcomes.

The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease
An appropriate screening approach and quality care are crucial for TB programmes in prisons. This study assessed crude TB prevalence, accuracy of the screening methods and treatment outcomes in a Thai prison. This was a retrospective analysis of fin...

World Health Organization's Early AI-supported Response with Social Listening Platform.

Journal of the Medical Library Association : JMLA
(WHO EARS). WHO HQ, Avenue Appia 20, 1211, Geneva 27, Switzerland; https://www.who-ears.com/; free.