Combining a Risk Factor Score Designed From Electronic Health Records With a Digital Cytology Image Scoring System to Improve Bladder Cancer Detection: Proof-of-Concept Study by Sandie Cabon, Sarra Brihi, Riadh Fezzani, Morgane Pierre-Jean, Marc Cuggia, Guillaume Bouzillé

“…This combination offers a higher associated risk of cancer than VisioCyt alone for all classes, especially for low-grade bladder cancer. …”
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Identification of a Prognosis-Related Risk Signature for Bladder Cancer to Predict Survival and Immune Landscapes by Linhui Wang, Yutao Wang, Jianfeng Wang, Luanfeng Li, Jianbin Bi

“…The potential biological functions of the selected genes were analyzed using CIBERSORT and ESTIMATE algorithms. Cancer Therapeutics Response Portal (CTRP) and PRISM datasets were used to identify drugs with high sensitivity. …”
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Interpretable machine learning for predicting isolated basal septal hypertrophy. by Lei Gao, Boyan Tian, Qiqi Jia, Xingyu He, Guannan Zhao, Yueheng Wang

“…The data were divided into training and test sets in a 7:3 ratio. Five machine learning algorithms -XGBoost, Random Forest(RF), Dicision tree(DT), K-Nearest Neighbor classification(KNN), and Naive Bayes(NB) were applied to construct the models, combined with logistic regression (LR) based on Lasso regression. …”
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A machine learning model for predicting obesity risk in patients with diabetes mellitus: analysis of NHANES 2007–2018 by Wenqiang Wang, Ruiqing Mo, Xingyu Chen, Sijie Yang

“…It also showed good discrimination (AUC = 0.751 in the training set and 0.781 in the test set), favorable calibration, and consistent clinical utility based on decision curve analysis (DCA). …”
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Construction and interpretation of tobacco leaf position discrimination model based on interpretable machine learning by Ranran Kou, Cong Wang, Jinxia Liu, Ran Wan, Zhe Jin, Le Zhao, Youjie Liu, Junwei Guo, Feng Li, Hongbo Wang, Song Yang, Cong Nie

“…Tobacco leaf position is closely associated with its quality whose material basis is the chemical components of tobacco leaf. …”
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AirQuaNet: A Convolutional Neural Network Model With Multi-Scale Feature Learning and Attention Mechanisms for Air Quality-Based Health Impact Prediction by Sreeni Chadalavada, Suleyman Yaman, Abdulkadir Sengur, Ravinesh C. Deo, Abdul Hafeez-Baig, Tracy Kolbe-Alexander, Niranjana Sampathila, U. Rajendra Acharya

“…Predicting air quality and associated health impacts is crucial for early intervention and informed policy planning. …”
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In Vitro Oral Cavity Permeability Assessment to Enable Simulation of Drug Absorption by Pankaj Dwivedi, Priyata Kalra, Haiying Zhou, Khondoker Alam, Eleftheria Tsakalozou, Manar Al-Ghabeish, Megan Kelchen, Giovanni M. Pauletti

“…<b>Conclusions</b>: Experimental permeation data collected for selected APIs in FDA-approved oral cavity products will serve as a training set to aid the development of predictive computational models for improving algorithms that describe drug absorption from the oral cavity. …”
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The role of artificial intelligence in promoting health and developing preventive strategies for diabetes by Ameneh Marzban

“…Moreover, challenges such as algorithmic bias, generalizability across diverse populations, and the necessity for clinician training must be carefully considered. …”
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Clinical prediction of intravenous immunoglobulin-resistant Kawasaki disease based on interpretable Transformer model. by Gahao Chen, Ziwei Yang

“…A cohort of 1,578 pediatric KD cases was systematically divided into training and validation sets. Six machine learning algorithms - Random Forest (RF), AdaBoost, Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Tabular Prior-data Fitted Network version 2.0 (TabPFN-V2) - were implemented with five-fold cross-validation to optimize model hyperparameters. …”
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TILTomorrow today: dynamic factors predicting changes in intracranial pressure treatment intensity after traumatic brain injury by Shubhayu Bhattacharyay, Florian D. van Leeuwen, Erta Beqiri, Cecilia A. I. Åkerlund, Lindsay Wilson, Ewout W. Steyerberg, David W. Nelson, Andrew I. R. Maas, David K. Menon, Ari Ercole, the CENTER-TBI investigators and participants

“…With 20 repeats of fivefold cross-validation, we trained TILTomorrow on different variable sets and applied the TimeSHAP (temporal extension of SHapley Additive exPlanations) algorithm to estimate variable contributions towards predictions of next-day changes in TIL(Basic). …”
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Research advancements in the Use of artificial intelligence for prenatal diagnosis of neural tube defects by Maryam Yeganegi, Mahsa Danaei, Sepideh Azizi, Fatemeh Jayervand, Reza Bahrami, Seyed Alireza Dastgheib, Heewa Rashnavadi, Ali Masoudi, Amirmasoud Shiri, Kazem Aghili, Mahood Noorishadkam, Hossein Neamatzadeh

“…AI integration with genomic analysis has identified key biomarkers associated with NTDs, such as Growth Associated Protein 43 (GAP43) and Glial Fibrillary Acidic Protein (GFAP), with logistic regression models achieving 86.67% accuracy. …”
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Machine Learning-Driven Prediction of One-Year Readmission in HFrEF Patients: The Key Role of Inflammation by Ma F, Hu Y, Han P, Qiu Y, Liu Y, Ren J

“…Seven machine learning (ML) algorithms were trained and validated using a 7:3 dataset split; the metrics of the model included the area under the curve (AUC), accuracy, sensitivity, specificity, F1 score, and Brier score. …”
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Widespread use of ChatGPT and other Artificial Intelligence tools among medical students in Uganda: A cross-sectional study. by Elizabeth Ajalo, David Mukunya, Ritah Nantale, Frank Kayemba, Kennedy Pangholi, Jonathan Babuya, Suzan Langoya Akuu, Amelia Margaret Namiiro, Yakobo Baddokwaya Nsubuga, Joseph Luwaga Mpagi, Milton W Musaba, Faith Oguttu, Job Kuteesa, Aloysius Gonzaga Mubuuke, Ian Guyton Munabi, Sarah Kiguli

“…<h4>Background</h4>Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that uses deep learning algorithms trained on vast amounts of data to generate human-like texts such as essays. …”
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Polyamine metabolism related gene index prediction of prognosis and immunotherapy response in breast cancer by Ruoya Wang, Shouliang Cai, Qing Gao, Yidong Chen, Xue Han, Fangjian Shang, Chunyan Liang, Guolian Zhu, Bo Chen

“…BackgroundPolyamine metabolism is closely associated with tumorigenesis, progression, and the tumor microenvironment (TME). …”
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The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT by Krzysztof Wach, Cong Doanh Duong, Joanna Ejdys, Rūta Kazlauskaitė, Pawel Korzynski, Grzegorz Mazurek, Joanna Paliszkiewicz, Ewa Ziemba

“… Objective: The objective of the article is to provide a comprehensive identification and understanding of the challenges and opportunities associated with the use of generative artificial intelligence (GAI) in business. …”
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Identification and Validation of Ferritinophagy-Related Biomarkers in Periodontitis by Yi-Ming Li, Chen‑Xi Li, Reyila Jureti, Gulinuer Awuti

“…This study aimed to investigate the biomarkers associated with ferritinophagy in periodontitis using transcriptomic data. …”
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Abstract 268: Validation of an automated method to assess 24‐hour infarct volume in stroke patients by Magda Jablonska, João André Sousa, Marc Molina, Pere Canals, Alvaro Garcia‐Tornel, Marc Rodrigo‐Gisbert, Federica Rizzo, Marta Olivé‐Gadea, Manuel Requena, David Rodriguez‐Luna, Noelia Rodriguez‐Villatoro, Jesús M. Juega, Marián Muchada, Jorge Pagola, Marta Rubiera, Alejandro Tomasello, Carlos A. Molina, Leonardo Tanzi, Victor Salvia, Cristian Marti, Marc Ribo

“…We aimed to validate an automated machine learning algorithm (MethinksFIV) specifically trained to automatically segment and measure subacute infarcts on non‐contrast CT (NCCT) scans. …”
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Critical role of Oas1g and STAT1 pathways in neuroinflammation: insights for Alzheimer’s disease therapeutics by Zhixin Xie, Linxi Li, Weizhong Hou, Zhongxi Fan, Lifan Zeng, Limin He, Yunxiang Ji, Jingbai Zhang, Fangran Wang, Zhou Xing, Yezhong Wang, Yongyi Ye

“…Weighted Correlation Network Analysis was used on the training dataset to identify gene co-expression networks. …”
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Disease activity and treatment response in early rheumatoid arthritis: an exploratory metabolomic profiling in the NORD-STAR cohort by Tahzeeb Fatima, Yuan Zhang, Georgios K. Vasileiadis, Araz Rawshani, Ronald van Vollenhoven, Jon Lampa, Bjorn Gudbjornsson, Espen A. Haavardsholm, Dan Nordström, Gerdur Gröndal, Kim Hørslev-Petersen, Kristina Lend, Marte S. Heiberg, Merete Lund Hetland, Michael Nurmohamed, Mikkel Østergaard, Till Uhlig, Tuulikki Sokka-Isler, Anna Rudin, Cristina Maglio

“…The best predictive model using logistic regression achieved AUC of 0.75 in training and 0.73 in the test set. Conclusions Our study has identified several baseline metabolites and metabolic pathways associated with disease activity and response to different treatments in early RA. …”
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Deep learning-based detection and classification of acute lymphoblastic leukemia with explainable AI techniques by Debendra Muduli, Sourav Parija, Suhani Kumari, Asmaul Hassan, Harendra S. Jangwan, Abu Taha Zamani, Sk. Mohammed Gouse, Banshidhar Majhi, Nikhat Parveen

“…A detailed comparative analysis was conducted, examining key parameters such as learning rate, optimization algorithms, and the number of training epochs to determine the most effective approach. …”
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