Artificial intelligence in dentistry: Assessing the informational quality of YouTube videos.

PLoS One

Dental Health Department, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia.

Published: January 2025

Background And Purpose: The most widely used social media platform for video content is YouTubeTM. The present study evaluated the quality of information on YouTubeTM on artificial intelligence (AI) in dentistry.

Methods: This cross-sectional study used YouTubeTM (https://www.youtube.com) for searching videos. The terms used for the search were "artificial intelligence in dentistry," "machine learning in dental care," and "deep learning in dentistry." The accuracy and reliability of the information source were assessed using the DISCERN score. The quality of the videos was evaluated using the modified Global Quality Score (mGQS) and the Journal of the American Medical Association (JAMA) score.

Results: The analysis of 91 YouTube™ videos on AI in dentistry revealed insights into video characteristics, content, and quality. On average, videos were 22.45 minutes and received 1715.58 views and 23.79 likes. The topics were mainly centered on general dentistry (66%), with radiology (18%), orthodontics (9%), prosthodontics (4%), and implants (3%). DISCERN and mGQS scores were higher for videos uploaded by healthcare professionals and educational content videos(P<0.05). DISCERN exhibited a strong correlation (0.75) with the video source and with JAMA (0.77). The correlation of the video's content and mGQS, was 0.66 indicated moderate correlation.

Conclusion: YouTube™ has informative and moderately reliable videos on AI in dentistry. Dental students, dentists and patients can use these videos to learn and educate about artificial intelligence in dentistry. Professionals should upload more videos to enhance the reliability of the content.

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0316635PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11695022PMC

Publication Analysis

Top Keywords

artificial intelligence
8
videos
6
quality
5
intelligence dentistry
4
dentistry assessing
4
assessing informational
4
informational quality
4
quality youtube
4
youtube videos
4
videos background
4

Similar Publications

Weighted Echo State Graph Neural Networks Based on Robust and Epitaxial Film Memristors.

Adv Sci (Weinh)

January 2025

College of Physics Science & Technology, School of Life Sciences, Institute of Life Science and Green Development, Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei University, Baoding, 071002, China.

Hardware system customized toward the demands of graph neural network learning would promote efficiency and strong temporal processing for graph-structured data. However, most amorphous/polycrystalline oxides-based memristors commonly have unstable conductance regulation due to random growth of conductive filaments. And graph neural networks based on robust and epitaxial film memristors can especially improve energy efficiency due to their high endurance and ultra-low power consumption.

View Article and Find Full Text PDF

The COVID-19 pandemic provided an ideal scenario for studying the care of the elderly population, we implemented a tool named the Geriatric Measure (GM) tool to determine the severity and need for hospitalization. The objective of the study is to evaluate if the results of a brief Geriatric Measure tool are associated with mortality and other outcomes among older adults with COVID-19 treated in the emergency department. Retrospective observational cohort study.

View Article and Find Full Text PDF

Machine learning techniques for non-destructive estimation of plum fruit weight.

Sci Rep

January 2025

Crop and Horticultural Science Research Department, Mazandaran Agricultural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Tajrish, Iran.

Plum fruit fresh weight (FW) estimation is crucial for various agricultural practices, including yield prediction, quality control, and market pricing. Traditional methods for estimating fruit weight are often destructive, time-consuming, and labor-intensive. In this study, we addressed the problem of predicting plum FW using artificial intelligence (AI) methods based on fruit dimensions.

View Article and Find Full Text PDF

Cancer-associated fibroblasts (CAFs) significantly influence tumor progression and therapeutic resistance in colorectal cancer (CRC). However, the distributions and functions of CAF subpopulations vary across the four consensus molecular subtypes (CMSs) of CRC. This study performed single-cell RNA and bulk RNA sequencing and revealed that myofibroblast-like CAFs (myCAFs), tumor-like CAFs (tCAFs), inflammatory CAFs (iCAFs), CXCL14CAFs, and MTCAFs are notably enriched in CMS4 compared with other CMSs of CRC.

View Article and Find Full Text PDF

Graph data is essential for modeling complex relationships among entities. Graph Neural Networks (GNNs) have demonstrated effectiveness in processing low-order undirected graph data; however, in complex directed graphs, relationships between nodes extend beyond first-order connections and encompass higher-order relationships. Additionally, the asymmetry introduced by edge directionality further complicates node interactions, presenting greater challenges for extracting node information.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!