Unlabelled: This review aims to analyze different strategies that make use of artificial intelligence to enhance diagnosis, treatment planning, and monitoring in orthodontics. Orthodontics has seen significant technological advancements with the introduction of digital equipment, including cone beam computed tomography, intraoral scanners, and software coupled to these devices. The use of deep learning in software has sped up image processing processes. Deep learning is an artificial intelligence technology that trains computers to analyze data like the human brain does. Deep learning models are capable of recognizing complex patterns in photos, text, audio, and other data to generate accurate information and predictions.
Materials And Methods: Pubmed, Scopus, and Web of Science were used to discover publications from 1 January 2013 to 18 October 2023 that matched our topic. A comparison of various artificial intelligence applications in orthodontics was generated.
Results: A final number of 33 studies were included in the review for qualitative analysis.
Conclusions: These studies demonstrate the effectiveness of AI in enhancing orthodontic diagnosis, treatment planning, and assessment. A lot of articles emphasize the integration of artificial intelligence into orthodontics and its potential to revolutionize treatment monitoring, evaluation, and patient outcomes.
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http://dx.doi.org/10.3390/diagnostics13243677 | DOI Listing |
Croat Med J
December 2024
Hrvoje Barić, Croatian Medical Journal, Zagreb, Croatia,
J Chem Theory Comput
January 2025
Advanced Artificial Intelligence Theoretical and Computational Chemistry Laboratory, School of Chemistry, University of Hyderabad, Hyderabad, Telangana 500046, India.
We present a directed electrostatics strategy integrated as a graph neural network (DESIGNN) approach for predicting stable nanocluster structures on their potential energy surfaces (PESs). The DESIGNN approach is a graph neural network (GNN)-based model for building structures of large atomic clusters with specific sizes and point-group symmetry. This model assists in the structure building of atomic metal clusters by predicting molecular electrostatic potential (MESP) topography minima on their structural evolution paths.
View Article and Find Full Text PDFCancer Med
January 2025
Department of Pharmacology, College of Pharmacy, Jinan University, Guangzhou, China.
Background: Distinctive heterogeneity characterizes diffuse large B-cell lymphoma (DLBCL), one of the most frequent types of non-Hodgkin's lymphoma. Mitochondria have been demonstrated to be closely involved in tumorigenesis and progression, particularly in DLBCL.
Objective: The purposes of this study were to identify the prognostic mitochondria-related genes (MRGs) in DLBCL, and to develop a risk model based on MRGs and machine learning algorithms.
J Educ Health Promot
November 2024
Department of Medicine, Clinical Education Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
ChatGPT has demonstrated significant potential in various aspects of medicine, including its performance on licensing examinations. In this study, we systematically investigated ChatGPT's performance in Iranian medical exams and assessed the quality of the included studies using a previously published assessment checklist. The study found that ChatGPT achieved an accuracy range of 32-72% on basic science exams, 34-68.
View Article and Find Full Text PDFCureus
January 2025
Consultation-Liaison Psychiatry, York and Scarborough Teaching Hospitals NHS Foundation Trust, York, GBR.
Skin cancers are among the most common cancers in the Western world, with incidence rates increasing significantly over time. Skin cancer survival rates are highly dependent upon early identification. In the United Kingdom (UK), initial assessment of skin lesions is carried out via general practitioners (GPs) who identify and refer suspected cases under the two-week pathway in compliance with the National Institute for Health and Care Excellence (NICE) guidelines.
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