Feasibility study of automatic radiotherapy treatment planning for cervical cancer using a large language model by Shuoyang Wei, Ankang Hu, Yongguang Liang, Jingru Yang, Lang Yu, Wenbo Li, Bo Yang, Jie Qiu

“…The emergence of artificial intelligence, particularly Large Language Models (LLMs), surpassing human capabilities and existing algorithms in various domains, presents an opportunity to automate and enhance this optimization process. …”
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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

“…Key research themes focus on autonomous mobility, traffic optimization, and sustainable transportation networks. …”
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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

“…This is the first worldwide study proving the accuracy of a ML model for differentiation of motility patterns in HR-ARM, demonstrating the value of artificial intelligence models in optimizing HR-ARM availability while reducing interobserver variability and increasing accuracy.…”
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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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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

“…The optimal PMPG (PVC:DBA = 1:5, 1 wt% MWCNTs) exhibited outstanding performance. …”
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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

“…Artificial intelligence (AI) and machine learning (ML) are transforming the management of acute appendicitis by enhancing diagnostic accuracy, optimizing treatment strategies, and improving patient outcomes. …”
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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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Enhanced Feature Selection via Hierarchical Concept Modeling by Jarunee Saelee, Patsita Wetchapram, Apirat Wanichsombat, Arthit Intarasit, Jirapond Muangprathub, Laor Boongasame, Boonyarit Choopradit

“…Moreover, this study is considered to provide a mathematical feature selection approach with optimization based on a paired-samples <i>t</i>-test. …”
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Blockchain Approach for Healthcare Using Fog Topology and Lightweight Consensus by Aya Laouamri, Sarra Cherbal, Yacine Mosbah, Chahrazed Benrebbouh, Kamir Kharoubi

“…The proposed architecture addresses critical challenges and offers practical benefits such as resource efficiency and system stability. While promising, the framework requires real-world testing and further optimization to overcome potential scalability bottlenecks in large-scale healthcare deployments.…”
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Beyond Linearity: Uncovering the Complex Spatiotemporal Drivers of New-Type Urbanization and Eco-Environmental Resilience Coupling in China’s Chengdu–Chongqing Economic Circle with... by Caoxin Chen, Shiyi Wang, Meixi Liu, Ke Huang, Qiuyi Guo, Wei Xie, Jiangjun Wan

“…A random forest (RF) model, interpreted with Shapley Additive exPlanations (SHAP) and Partial Dependence Plot (PDP) algorithms, explores nonlinear driving mechanisms, while Geographically and Temporally Weighted Regression (GTWR) assesses drivers’ spatiotemporal heterogeneity. …”
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Advanced Interpretation of Bullet-Affected Chest X-Rays Using Deep Transfer Learning by Shaheer Khan, Nirban Bhowmick, Azib Farooq, Muhammad Zahid, Sultan Shoaib, Saqlain Razzaq, Abdul Razzaq, Yasar Amin

“…Special deep learning algorithms went through a process of optimization before researchers improved their ability to detect and place objects. …”
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Artificial Intelligence in Primary Malignant Bone Tumor Imaging: A Narrative Review by Platon S. Papageorgiou, Rafail Christodoulou, Panagiotis Korfiatis, Dimitra P. Papagelopoulos, Olympia Papakonstantinou, Nancy Pham, Amanda Woodward, Panayiotis J. Papagelopoulos

“…Through machine learning and deep learning techniques, AI leverages computational algorithms and large datasets to enhance medical imaging interpretation and support clinical decision-making. …”
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Cross-sectional and longitudinal Biomarker extraction and analysis for multicentre FLAIR brain MRI by J. DiGregorio, A. Gibicar, H. Khosravani, P. Jabehdar Maralani, J.-C. Tardif, P.N. Tyrrell, A.R. Moody, A. Khademi

“…Large-scale, automated cross-sectional and longitudinal cerebral biomarker extraction from FLAIR datasets could progress disease characterization, improve disease monitoring, and help to determine optimal intervention times. Despite this, most automated biomarker extraction algorithms are designed for T1-weighted or multi-modal inputs. …”
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Implications of machine learning techniques for prediction of motor health disorders in Saudi Arabia by Ehab M. Almetwally, I. Elbatal, Mohammed Elgarhy, Amr R. Kamel

“…This system is an efficient tool that properly detects and diagnoses a variety of motor impairment problems using ML algorithms. Decisions are made easier and social health care is improved with the help of this system because timely interventions are implemented, patient outcomes are improved, and resource allocation is optimized.…”
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Estimation and trend analysis of grassland aboveground biomass on the Qinghai-Xizang Plateau based on machine learning by Ruoqi Zhang, Qisheng Feng, Yonghui Zhang, Jingjing Mai, Tiangang Liang

“…The optimal model was identified through an exhaustive comparison and hyperparameter optimization process, and then applied to estimate grassland AGB across the plateau and analyze its spatiotemporal dynamics. …”
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Comparative study of machine learning methods for mapping forest fire areas using Sentinel-1B and 2A imagery by Xinbao Chen, Xinbao Chen, Yaohui Zhang, Shan Wang, Zecheng Zhao, Chang Liu, Junjun Wen

“…The combination of spectral and Normalized Difference Vegetation Index (NDVI) features achieved an optimal accuracy for identifying forest areas with an overall accuracy (OA) of 93.52%. …”
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Diagnostic Models for Differentiating COVID-19-Related Acute Ischemic Stroke Using Machine Learning Methods by Eylem Gul Ates, Gokcen Coban, Jale Karakaya

“…These findings demonstrate the potential of AI-driven diagnostic tools to identify high-risk patients, support optimized treatment strategies, and ultimately improve clinical implications.…”
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Development and validation of a machine learning model for online predicting the risk of in heart failure: based on the routine blood test and their derived parameters by Jianchen Pu, Yimin Yao, Xiaochun Wang

“…This online forecasting tool not only processes a large amount of data but also continuously optimizes and adjusts the accuracy of the model according to the latest medical research and clinical data. …”
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Long-term planning optimisation of sustainable energy systems: A systematic review and meta-analysis of trends, drivers, barriers, and prospects by Soheil Mohseni, Alan C. Brent

“…Nevertheless, despite the well-documented drivers that promote the adoption of renewable energy, an array of social, technical, economic, and regulatory barriers poses significant impediments to the realisation of optimally sized and operated renewable and sustainable energy systems. …”
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Assessing the temporal transferability of machine learning models for predicting processing pea yield and quality using Sentinel-2 and ERA5-land data by Michele Croci, Manuele Ragazzi, Alessandro Grassi, Giorgio Impollonia, Stefano Amaducci

“…Accurate pre-harvest prediction of yield and quality (tenderometric reading, TR) is crucial for the processing pea industry due to a narrow optimal harvest window. Machine learning (ML) models offer potential, but their real-world utility depends on their performance stability across different years. …”
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