AI-Driven Advancements in Orthodontics for Precision and Patient Outcomes by David B. Olawade, Navami Leena, Eghosasere Egbon, Jeniya Rai, Aysha P. E. K. Mohammed, Bankole I. Oladapo, Stergios Boussios

“…The review also highlights future prospects, such as the integration of AI with robotics for performing orthodontic procedures, predictive orthodontics for early intervention, and the use of 3D printing technologies to fabricate orthodontic devices in real-time. While AI offers tremendous potential, challenges remain in areas such as data privacy, algorithmic bias, and the cost of adopting AI technologies. …”
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Predicting rapid kidney function decline in middle-aged and elderly Chinese adults using machine learning techniques by Yang Li, Kun Zou, Yixuan Wang, Yucheng Zhang, Jingtao Zhong, Wu Zhou, Fang Tang, Lu Peng, Xusheng Liu, Lili Deng

“…Specifically, it attained AUC values of 0.8 and 0.765 in the training and test sets, respectively, while achieving accuracy scores of 0.736 and 0.728 in these two datasets. …”
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An intelligible AI-driven decision support system for poststroke mobility assessment by Jin Cheng Liaw, Dominik Raab, Malte Weber, Mario Siebler, Harald Hefter, Dörte Zietz, Marcus Jäger, Andrés Kecskeméthy, Francisco Geu Flores

“…To meet these challenges, we show that machine learning algorithms can reproduce expert mobility assessment from gait data with acceptable accuracy, supporting poststroke evaluation while giving intelligible feedback into how the assessments were generated. …”
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Time to Disease Control with Dupilumab for Bullous Pemphigoid: A Systematic Review and Meta-analysis by Laurence Mainville, Lily Xu, Vincent Piguet, Aaron M. Drucker, David O. Croitoru

“…Randomized controlled trials are needed to determine dupilumab’s place in therapeutic algorithms for BP. PROSPERO number: CRD42024599235 …”
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Large Language Models in Transportation: A Comprehensive Bibliometric Analysis of Emerging Trends, Challenges, and Future Research by Mahbub Hassan, Md. Emtiaz Kabir, Muzammil Jusoh, Hong Ki An, Michael Negnevitsky, Chengjiang Li

“…The findings highlight global disparities in research contributions, with China and the United States dominating by publication volume, followed by Germany and Canada, while developing regions exhibit lower scientific productivity. …”
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Artificial intelligence as a transforming factor in motility disorders–automatic detection of motility patterns in high-resolution anorectal manometry by Miguel Mascarenhas, Francisco Mendes, Joana Mota, Tiago Ribeiro, Pedro Cardoso, Miguel Martins, Maria João Almeida, João Rala Cordeiro, João Ferreira, Guilherme Macedo, Cecilio Santander

“…A dataset of 701 HR-ARM exams from a tertiary center, classified according to London Classification, was used to develop and test multiple machine learning (ML) algorithms. The exams were divided in a training and testing dataset with a 80/20% ratio. …”
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Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases by Huai-wen Zhang, Yi-ren Wang, Bo Hu, Bo Song, Zhong-jian Wen, Lei Su, Xiao-man Chen, Xi Wang, Ping Zhou, Xiao-ming Zhong, Hao-wen Pang, You-hua Wang

“…By using various machine-learning algorithms, including random forest, support vector machine, gradient boosting machine, XGBoost, decision tree, artificial neural network, k-nearest neighbors, LightGBM, and CatBoost algorithms, a stacking ensemble model was developed to classify gross tumor volume (GTV), brainstem, and normal brain tissue based on radiomic features. …”
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Investigating Spatial Effects through Machine Learning and Leveraging Explainable AI for Child Malnutrition in Pakistan by Xiaoyi Zhang, Muhammad Usman, Ateeq ur Rehman Irshad, Mudassar Rashid, Amira Khattak

“…Third, XGBoost and Random Forest machine learning algorithms were employed to examine and validate the importance of the spatial lag component. …”
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Artificial Intelligence and Ethical Dimensions of Automated Traffic Enforcement: Implications for Public Health, Healthcare Equity, and Social Justice by Patricia Haley

“…This study provides a critical examination of AI-integrated speed and red-light camera systems through the theoretical lenses of Surveillance Capitalism, the Panopticon Model, Social Control Theory, Technological Determinism, and Structural Violence Theory. While artificial intelligent speed safety cameras demonstrate efficacy in reducing traffic violations and fatalities, this research addresses a critical gap in healthcare literature regarding their broader societal and ethical consequences, including algorithmic bias, data governance failures, and privacy violations that directly impact public trust and health equity. …”
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Assessment of binary prediction of fraudulent advertisements in ATS candidate tracking cloud systems by V. V. Ligi-Goryaev, G. A. Mankaeva, T. B. Goldvarg, S. S. Muchkaeva, V. V. Dzhakhnaev

“…Various machine learning algorithms can be employed to address this issue. Traditional classification algorithms, including LSVC (Support Vector Machine), GBT (Gradient Boosting Tree), and RF (Random Forest), have been chosen for this study. …”
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Development of an ensemble prediction model for acute graft-versus-host disease in allogeneic transplantation based on machine learning by Lin Song, Xingwei Wu, Mengjia Xu, Ling Xue, Xun Yu, Zongqi Cheng, Chenrong Huang, Liyan Miao

“…Meanwhile, correlation analysis and recursive feature elimination were used for feature screening before machine learning model development. Then fifteen algorithms were used to establish models, and an ensemble model was established through soft voting based on the top five performance algorithms. …”
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Data Confidentiality in Artificial Intelligence-Assisted Medicine: The Main Ethical Issue by Raluca DUMACHE, Camelia MURESAN, Marina Adriana MERCIONI, Alin IONESCU, Nina IVANOVIC, Alexandra ENACHE

“…Materials and Methods: The study entails gathering patient data from electronic health records (EHRs), anonymizing it, creating AI algorithms for recommending treatments and diagnosing diseases, putting strong security measures in place, abiding by ethical standards, identifying and reducing biases in AI algorithms, and making sure that laws like GDPR and HIPAA are followed. …”
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Exploring the best fit: A comparative analysis of AFINN, Textblob, VADER, and Pattern on Arabic reviews for optimal dictionary extraction by Shakeel Ahmad, Sheikh Muhammad Saqib, Asif Hassan Syed, Nashwan Alromema, Ali Kararay

“…However, in the context of the Arabic language, studies predominantly resort to machine learning or deep learning algorithms for sentiment and emotion analysis, often neglecting the utilization of current pre-trained language models. …”
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Advanced Classifiers and Feature Reduction for Accurate Insomnia Detection Using Multimodal Dataset by Ameya Chatur, Mostafa Haghi, Nagarajan Ganapathy, Nima TaheriNejad, Ralf Seepold, Natividad Martinez Madrid

“…Our results demonstrate that the ensemble classifiers generalize well on the dataset regardless of the feature count, while other algorithms are hindered by the curse of dimensionality.…”
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Leveraging citizen science to classify and track benthic habitat states: An unsupervised UMAP-HDBSCAN pipeline applied to the global reef life survey dataset by Clément Violet, Aurélien Boyé, Stanislas Dubois, Graham J. Edgar, Elizabeth S. Oh, Rick D. Stuart-Smith, Martin P. Marzloff

“…Benthic biogenic habitats are crucial for coastal marine ecosystems, supporting food and shelter for a large range of marine species, but they are increasingly threatened by increasing anthropogenic impacts. While large-scale monitoring data are increasingly available, tools to describe benthic habitat changes in standardised and yet finely resolved manner are still needed. …”
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An in silico evaluation of lorlatinib as a potential therapy for novel amino acid substitutions in the tyrosine kinase domain of the ALK protein associated with cancer by Richard Junior Zapata Dongo, Julio A. Poterico, Diletta Fontana, Luca Mologni, Carla Alvarez-Chacon, Juan Rojas-Armas, Jaeson Calla

“…Using the SIFT and Polyphen-2 algorithms, we identified 53 deleterious ALK mutations associated with different newly recognized cancer types. …”
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A Piezoelectric Sensor Based on MWCNT-Enhanced Polyvinyl Chloride Gel for Contact Perception of Grippers by Qiyun Zhong, Qingsong He, Diyi Liu, Xinyu Lu, Siyuan Liu, Yuze Ye, Yefu Wang

“…By integrating PMPG with machine learning algorithms, soft robotic grippers gain advanced contact perception capabilities, enabling applications in medicine, rescue, exploration, and other fields requiring fine manipulation and adaptability. …”
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A Systematic Integration of Artificial Intelligence Models in Appendicitis Management: A Comprehensive Review by Ivan Maleš, Marko Kumrić, Andrea Huić Maleš, Ivan Cvitković, Roko Šantić, Zenon Pogorelić, Joško Božić

“…In diagnostics, ML algorithms incorporating clinical, laboratory, imaging, and demographic data have improved accuracy and reduced uncertainty. …”
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Literature Review of Prognostic Factors in Secondary Generalized Peritonitis by Valerii Luțenco, Adrian Beznea, Raul Mihailov, George Țocu, Verginia Luțenco, Oana Mariana Mihailov, Mihaela Patriciu, Grigore Pascaru, Liliana Baroiu

“…The Mannheim Peritonitis Index (MPI) remains a widely validated prognostic tool, while APACHE II and SOFA scores also provide valuable risk estimates. …”
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Steganographic model to conceal the secret data in audio files utilizing a fourfold paradigm: Interpolation, multi-layering, optimized sample space, and smoothing by Daffa Tristan Firdaus, Ntivuguruzwa Jean De La Croix, Tohari Ahmad, Didacienne Mukanyiligira, Louis Sibomana

“…These findings confirm the effectiveness of the model in maintaining audio quality while concealing data. Moreover, this study recommends that practitioners in steganography with audio data prioritize data concealment algorithms that enhance security over stego audio quality.…”
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