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World Health Organization

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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.

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...

Artificial intelligence for sustainable oral healthcare.

Journal of dentistry
OBJECTIVES: Oral health is grounded in the United National (UN) 2030 Agenda for Sustainable Developement and its 17 Goals (SDGs), in particular SDG 3 (Ensure healthy lives and promote well-being for all at all ages). The World Health Organization (WH...

The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review.

Frontiers in public health
BACKGROUND: Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the u...

Application of a developed triple-classification machine learning model for carcinogenic prediction of hazardous organic chemicals to the US, EU, and WHO based on Chinese database.

Ecotoxicology and environmental safety
Cancer, the second largest human disease, has become a major public health problem. The prediction of chemicals' carcinogenicity before their synthesis is crucial. In this paper, seven machine learning algorithms (i.e., Random Forest (RF), Logistic R...

Histopathological auxiliary system for brain tumour (HAS-Bt) based on weakly supervised learning using a WHO CNS5-style pipeline.

Journal of neuro-oncology
PURPOSE: Classification and grading of central nervous system (CNS) tumours play a critical role in the clinic. When WHO CNS5 simplifies the histopathology diagnosis and places greater emphasis on molecular pathology, artificial intelligence (AI) has...

Glioma Tumor Grading Using Radiomics on Conventional MRI: A Comparative Study of WHO 2021 and WHO 2016 Classification of Central Nervous Tumors.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Glioma grading transformed in World Health Organization (WHO) 2021 CNS tumor classification, integrating molecular markers. However, the impact of this change on radiomics-based machine learning (ML) classifiers remains unexplored.

A comprehensive segmentation of chest X-ray improves deep learning-based WHO radiologically confirmed pneumonia diagnosis in children.

European radiology
OBJECTIVES: To investigate a comprehensive segmentation of chest X-ray (CXR) in promoting deep learning-based World Health Organization's (WHO) radiologically confirmed pneumonia diagnosis in children.