Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews

Martínez-Millana, Antonio; Sáez, Aida Martínez; Tornero, Roberto; Azzopardi-Muscat, Natasha; Traver, Vicente; Novillo-Ortiz, David

doi:10.1016/j.ijmedinf.2022.104855

Cited by 26 publications

(11 citation statements)

References 153 publications

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“… 6 Despite this, there is a very significant gap between the development of models in a laboratory setting, i.e., in a scientific environment, and the implementation of these models in real conditions. 7 …”

Section: What Do We Mean By Artificial Intelligence?mentioning

confidence: 99%

The artificial intelligence revolution in primary care: Challenges, dilemmas and opportunities

Vidal-Alaball,

Panadés Zafra,

Escalé-Besa

et al. 2024

Atención Primaria

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Section: What Do We Mean By Artificial Intelligence?mentioning

confidence: 99%

The artificial intelligence revolution in primary care: Challenges, dilemmas and opportunities

Vidal-Alaball,

Panadés Zafra,

Escalé-Besa

et al. 2024

Atención Primaria

Self Cite

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“…According to a systematic literature review, AI is predominantly utilized to achieve UHC, among other health agendas [ 8 ]. Mongolia has taken steps towards embracing AI in healthcare.…”

Section: Reality and Potential Of Ai For Uhc Including Radiology Exam...mentioning

confidence: 99%

Role of Artificial Intelligence in Achieving Universal Health Coverage: A Mongolian Perspective

Bold,

Lkhagvajav,

Dorjsuren

2023

Korean J Radiol

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“…The use of ultrasound imaging for detecting and diagnosing breast cancer has been significantly improved with the introduction of machine learning (ML) and deep learning (DL) techniques. Compared with other ML techniques, DL algorithms, such as convolutional neural networks (CNNs), show increased potential and reliability in diagnosing disease [5,[11][12][13][14][15]. In the case of segmentation, these algorithms can be trained on large datasets to find boundaries by recognizing their features, such as shape, texture, and color.…”

Section: Introductionmentioning

confidence: 99%

WATUNet: a deep neural network for segmentation of volumetric sweep imaging ultrasound

Khaledyan,

Marini,

O’Connell

et al. 2024

Mach. Learn.: Sci. Technol.

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Objective.Limited access to breast cancer diagnosis globally leads to delayed treatment. Ultrasound, an effective yet underutilized method, requires specialized training for sonographers, which hinders its widespread use. Approach. Volume sweep imaging (VSI) is an innovative approach that enables untrained operators to capture high-quality ultrasound images. Combined with deep learning, like convolutional neural networks (CNNs), it can potentially transform breast cancer diagnosis, enhancing accuracy, saving time and costs, and improving patient outcomes. The widely used UNet architecture, known for medical image segmentation, has limitations, such as vanishing gradients and a lack of multi-scale feature extraction and selective region attention. In this study, we present a novel segmentation model known as Wavelet_Attention_UNet (WATUNet). In this model, we incorporate wavelet gates (WGs) and attention gates (AGs) between the encoder and decoder instead of a simple connection to overcome the limitations mentioned, thereby improving model performance.Main results.Two datasets are utilized for the analysis. The public "Breast Ultrasound Images" (BUSI) dataset of 780 images and a VSI dataset of 3818 images. Both datasets contained segmented lesions categorized into three types: no mass, benign mass, and malignant mass. Our segmentation results show superior performance compared to other deep networks. The proposed algorithm attained a Dice coefficient of 0.94 and an F1 score of 0.94 on the VSI dataset and scored 0.93 and 0.94 on the public dataset, respectively. Significance. The experimental findings demonstrate that the proposed WATUNet model achieves precise segmentation of breast lesions in both standard-of-care and VSI images, surpassing state-of-the-art models. Hence, the model holds considerable promise for assisting in lesion identification, an essential step in the clinical diagnosis of breast lesions.

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Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews

Cited by 26 publications

References 153 publications

The artificial intelligence revolution in primary care: Challenges, dilemmas and opportunities

The artificial intelligence revolution in primary care: Challenges, dilemmas and opportunities

Role of Artificial Intelligence in Achieving Universal Health Coverage: A Mongolian Perspective

WATUNet: a deep neural network for segmentation of volumetric sweep imaging ultrasound

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