Artificial Intelligence for Predicting Delivery Modes: A Systematic Review of Applications, Challenges, and Future Directions by Yu Zhou, Jing Li, Xiaomei Hou, Zhen Li, Yanan Xu, Yan Wang, Mingze Sun, Fumin Zheng, Enhui Guo, Jun Zhou

“…Conclusions: Future research should prioritize data standardization and sharing, enhance the generalizability of prediction models, address ethical considerations, and ensure the fairness and transparency of AI algorithms to improve clinical trust and applicability. …”
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Machine learning in lymphocyte and immune biomarker analysis for childhood thyroid diseases in China by Ruizhe Yang, Wei Li, Qing Niu, WenTao Yang, Wei Gu, Xu Wang

“…Additionally, nine machine learning (ML) algorithms were utilized to construct prediction models. …”
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Research on the development of an intelligent prediction model for blood pressure variability during hemodialysis by Zhijian Ren, Minqiao Zhang, Pingping Wang, Kanan Chen, Jing Wang, Lingping Wu, Yue Hong, Yihui Qu, Qun Luo, Kedan Cai

“…The definitions of IDH and IDHTN were clarified, and 10 machine learning algorithms were used to build the models. For model development, the dialysis data were randomly split into a training set (80%) and a testing set (20%). …”
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Monitoring and Comparative Analysis of NO₂ and HCHO in Shanghai Using Dual-Azimuth Scanning MAX-DOAS and TROPOMI by Hongmei Ren, Ang Li, Zhaokun Hu, Nannan Shao, Xinyan Yang, Hairong Zhang, Jiangman Xu, Jinji Ma

“…These findings not only reveal the spatiotemporal distribution characteristics of regional pollutants but optimize the sampling time and distance parameters for satellite–ground observation validation, providing data support for improving and enhancing the accuracy of satellite retrieval algorithms.…”
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Continuous heart rate measurements in patients with cardiac disease: Device comparison and development of a novel artefact removal procedure by Paulien Vermunicht, Katsiaryna Makayed, Christophe Buyck, Lieselotte Knaepen, Juan Sebastian Piedrahita Giraldo, Sebastiaan Naessens, Wendy Hens, Emeline Van Craenenbroeck, Kris Laukens, Lien Desteghe, Hein Heidbuchel

“…Photoplethysmography (PPG)-based HR data provides near-continuous data, spanning longer periods, but improved algorithms to filter unreliable data are needed. Methods This observational, prospective pilot study compared the accuracy of two wearables for HR monitoring (electrocardiogram [ECG]-based Polar H10 chest strap and PPG-based Fitbit Inspire 2 wrist tracker) against Holter monitoring in 15 patients with atrial fibrillation (AF), heart failure (HF) and coronary artery disease referred for cardiac rehabilitation (CR). …”
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Ontological approach to modeling innovation processes on the example of a distributed educational network of the University by D. G. Korneev, M. S. Gasparian, A. A. Mikryukov

“…In this regard, the problem of modeling and algorithmization of the formation of educational programs as a chain of interrelated training modules and objects, taking into account the implementation of a competence-based model of learning in a distributed educational environment of the University, seems to be interesting.The modern education system is to be aimed primarily at adaptive generation of educational content, taking into account the needs of students and in accordance with constantly changing conditions and needs of the labor market. …”
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Tensor RT optimized driver drowsiness detection system using edge device by Chandramohan Dhasarathan, Sambasivam Gnanasekaran, Arnab Pattanayak, Gourav Kumar, Kartik Vig, Vaibhav Narain, K.M. Deva Narayan, Sunidhi Garg

“…The system uses transfer learning techniques for implementing CNN model algorithms to analyze live video from the camera module, allowing for real-time detection of driver behavior such as fatigue or distraction. …”
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The value of multi-phase CT based intratumor and peritumoral radiomics models for evaluating capsular characteristics of parotid pleomorphic adenoma by Qian Shen, Cong Xiang, Yongliang Han, Yongmei Li, Kui Huang

“…Quantitative radiomics features of the intratumoral and peritumoral regions of 2 mm and 5 mm on CT images were extracted, and radiomics models of Tumor, External2, External5, Tumor+ External2, and Tumor+External5 were constructed and used to train six different machine learning algorithms. Meanwhile, the prediction performances of different radiomics models (Tumor, External2, External5, Tumor+External2, Tumor+External5) based on single phase (plain, arterial, and venous phase) and multiphase (three-phase combination) were compared. …”
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Identifying Therapeutic Targets and Potential Drugs for Diabetic Retinopathy: Focus on Oxidative Stress and Immune Infiltration by Peng H, Hu Q, Zhang X, Huang J, Luo S, Zhang Y, Jiang B, Sun D

“…Analysis of protein-protein interaction (PPI) networks and machine learning algorithms were used to identify hub genes. Single-gene Gene Set Enrichment Analysis (GSEA) identified biological functions, while nomograms and ROC curves assessed diagnostic potential. …”
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Investigating Drug-Induced Thyroid Dysfunction Adverse Events Associated With Non-Selective RET Multi-Kinase Inhibitors: A Pharmacovigilance Analysis Utilizing FDA Adverse Event Re... by Meng Z, Song L, Wang S, Duan G

“…Disproportionality analysis using ROR, PRR, BCPNN, and EBGM algorithms consistently demonstrated a positive association between Sunitinib, Cabozantinib, and Lenvatinib with TD adverse events. …”
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Medical Device Failure Predictions Through AI-Driven Analysis of Multimodal Maintenance Records by Noorul Husna Abd Rahman, Khairunnisa Hasikin, Nasrul Anuar Abd Razak, Ayman Khallel Al-Ani, D. Jerline Sheebha Anni, Prabu Mohandas

“…Then, four machine learning algorithms and three deep learning networks are evaluated to determine the best predictive model. …”
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MEMS and IoT in HAR: Effective Monitoring for the Health of Older People by Luigi Bibbò, Giovanni Angiulli, Filippo Laganà, Danilo Pratticò, Francesco Cotroneo, Fabio La Foresta, Mario Versaci

“…The analysis methods employed include machine learning algorithms to identify movement patterns, statistical analysis to assess the frequency and quality of movements, and data visualization to track changes over time. …”
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Constructing a Glioblastoma Prognostic Model Related to Fatty Acid Metabolism Using Machine Learning and Identifying F13A1 as a Potential Target by Yushu Liu, Hui Deng, Ping Song, Mengxian Zhang

“…On the basis of 10 kinds of machine learning methods, we used 101 combinations of algorithms to construct prognostic models and obtain the best model. …”
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Correlation between metformin use and mortality in acute respiratory failure: a retrospective ICU cohort study by Yunlin Yang, Jinfeng Liu, Yi Hou, Yuxun Wei, Liang Huang, Liang Huang, Wei Wei

“…Propensity score matching (PSM) and machine learning algorithms were used for confounder adjustment and feature selection.ResultsAfter PSM, 1,429 patients with ARF were included (374 metformin users; 1,055 non-users). …”
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Prognosis and immune landscape of bladder cancer can be predicted using a novel miRNA signature associated with cuproptosis by Zhilei Zhang, Fang Liu, Yongbo Yu, Fei Xie, Tao Zhu

“…We evaluated the tumor microenvironment (TME) of every patient using immune ESTIMATE, CIBERSORT, and ssGSEA algorithms. We also investigated the differences in tumor mutation burden (TMB) and drug sensitivity between two groups. …”
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A comprehensive investigation of morphological features responsible for cerebral aneurysm rupture using machine learning by Mostafa Zakeri, Amirhossein Atef, Mohammad Aziznia, Azadeh Jafari

“…To achieve this, we used 5 classification machine learning algorithms and trained them on a substantial database comprising 708 cerebral aneurysms. …”
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Factors influencing the response to periodontal therapy in patients with diabetes: post hoc analysis of a randomized clinical trial using machine learning by Nidia CASTRO DOS SANTOS, Arthur MANGUSSI, Tiago RIBEIRO, Rafael Nascimento de Brito SILVA, Mauro Pedrine SANTAMARIA, Magda FERES, Thomas VAN DYKE, Ana Carolina LORENA

“…We tested seven different algorithms: K-Nearest Neighbors, Decision Tree, Support Vector Machine, Random Forest, Extreme Gradient Boosting, and Logistic Regression. …”
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Predicting chronic kidney disease progression using small pathology datasets and explainable machine learning models by Sandeep Reddy, Supriya Roy, Kay Weng Choy, Sourav Sharma, Karen M Dwyer, Chaitanya Manapragada, Zane Miller, Joy Cheon, Bahareh Nakisa

“…Supervised classification modelling techniques included decision tree and random forest algorithms selected for interpretability. Internal validation on an Australian tertiary centre cohort (n = 706; 353 with kidney failure and 353 without) achieved exceptional predictive accuracy. …”
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Artificial intelligence (AI) in restorative dentistry: current trends and future prospects by Mariya Najeeb, Shahid Islam

“…Key challenges include data privacy concerns, algorithmic bias, interpretability of AI decision-making processes, and the need for standardized AI training programs in dental education. …”
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Complications after advanced ovarian cancer surgery—A population‐based cohort study by Charlotte Palmqvist, Hanna Michaëlsson, Christian Staf, Mia Johansson, Per Albertsson, Pernilla Dahm‐Kähler

“…These results may facilitate forming algorithms in the decision‐making procedure of surgical treatment protocols.…”
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