Artificial Intelligence–Aided Diagnosis System for the Detection and Classification of Private-Part Skin Diseases: Decision Analytical Modeling Study by Wei Wang, Xiang Chen, Licong Xu, Kai Huang, Shuang Zhao, Yong Wang

“…This system provides the final diagnosis result by simulating the diagnostic process of dermatologists. Compared with existing advanced algorithms, this system is more accurate in identifying PPSDs. …”
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Identification of Plasma Exosomes hsa_circ_0001360 and hsa_circ_0000038 as Key Biomarkers of Coronary Heart Disease by Wan Zhang, Jiasen Cui, Li Li, Ting Zhu, Zhenyu Guo

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Using data science to diagnose and characterize heterogeneity of Alzheimer's disease by Ting F.A. Ang, Ning An, Huitong Ding, Sherral Devine, Sanford H. Auerbach, Joseph Massaro, Prajakta Joshi, Xue Liu, Yulin Liu, Elizabeth Mahon, Rhoda Au, Honghuang Lin

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Applying Machine Learning Techniques to Identify Undiagnosed Patients with Exocrine Pancreatic Insufficiency by Bruce Pyenson, Maggie Alston, Jeffrey Gomberg, Feng Han, Nikhil Khandelwal, Motoharu Dei, Monica Son, Jaime Vora

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Identification of thyroid cancer biomarkers using WGCNA and machine learning by Gaofeng Hu, Wenyuan Niu, Jiaming Ge, Jie Xuan, Yanyang Liu, Mengjia Li, Huize Shen, Shang Ma, Yuanqiang Li, Qinglin Li

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Pulmonary hypertension associated with COPD: a phenotype analysis by Mathilde Steger, Matthieu Canuet, Guillaume Martin, Aissam Labani, Jean Charles Schwartz, Irina Enache, Armelle Schuller, Léo Meyer, Ari Chaouat, Romain Kessler, David Montani, Marianne Riou

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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

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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

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The Role of PLIN3 in Prognosis and Tumor-Associated Macrophage Infiltration: A Pan-Cancer Analysis by Yang S, Liu H, Zheng Y, Chu H, Lu Z, Yuan J, Xu S

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Machine Learning and Experimental Validation Reveal MYH11 as a Novel Prognostic Biomarker and Therapeutic Target in Bladder Cancer by Tan Z, Chen X, Fu S, Huang Y, Li H, Gong C, Lv D, Yang C, Wang J, Ding M, Wang H

“…Finally, single-cell analysis identified key cells involved in BCa pathogenesis, and in vitro experiments validated the expression and function of key genes.Results: The risk model constructed by 8 prognostic genes identified using 101 algorithms effectively predicted the survival outcomes of BCa patients. …”
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A novel biochemical analysis for ApoE4 quantification in plasma and discrimination of homozygous and heterozygous APOE ε4 carriers by Andrés Rodríguez, Olga Calero, Sergio Veiga, Miriam Menacho-Román, Ignacio Arribas, Lluís Cano, Guillermo García-Ribas, Miguel Calero

“…The test’s discriminatory performance was assessed by ROC analysis and two-threshold classification algorithms. Results ApoE4 levels ascertained by the e4Quant test exhibited clear genotype-dependent stratification. …”
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Effectiveness of AI for Enhancing Computed Tomography Image Quality and Radiation Protection in Radiology: Systematic Review and Meta-Analysis by Subo Zhang, Zhitao Zhu, Zhenfei Yu, Haifeng Sun, Yi Sun, Hai Huang, Lei Xu, Jinxin Wan

“…ConclusionsThe findings suggest that AI-based interventions can significantly enhance CT imaging practices by improving image quality and potentially reducing radiation doses, which may lead to better diagnostic accuracy and patient safety. However, these results should be interpreted with caution due to the limited number of studies and the variability in AI algorithms. …”
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Integration of Bulk and Single-Cell Transcriptomics Reveals BCL2L14 as a Novel IGKC+ T Cell-Associated Therapeutic Target in Breast Cancer by He J, Akhtar A, Li J, Wei Q, Yuan Y, Ran J, Ma Y, Chen D

“…Next, we applied bulk RNA-seq deconvolution algorithms to estimate the abundance of this subpopulation across breast cancer cohorts. …”
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The 2023 Bethesda System for Reporting Thyroid Cytopathology: A review update by A. Parmar Riddhi, Patel Keval

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Machine learning identification of key genes in cardioembolic stroke and atherosclerosis: their association with pan-cancer and immune cells by Tianxiang Zhang, Chunhui Yuan, Mo Chen, Jinjiang Liu, Wei Shao, Ning Cheng

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AI in Medical Questionnaires: Innovations, Diagnosis, and Implications by Xuexing Luo, Yiyuan Li, Jing Xu, Zhong Zheng, Fangtian Ying, Guanghui Huang

“…AI technology has now been shown to have great promise in improving diagnostic accuracy, assisting clinical decision-making, and simplifying questionnaire development and data analysis. …”
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Advancements in Herpes Zoster Diagnosis, Treatment, and Management: Systematic Review of Artificial Intelligence Applications by Dasheng Wu, Na Liu, Rui Ma, Peilong Wu

“…Classification tasks (85.2%) dominated AI applications, with neural networks, particularly multilayer perceptron and convolutional neural networks being the most frequently used algorithms. AI applications were analyzed across three domains: (1) diagnosis, where mobile deep neural networks, convolutional neural network ensemble models, and mixed-scale attention-based models have improved diagnostic accuracy and efficiency; (2) treatment, where machine learning models, such as deep autoencoders combined with functional magnetic resonance imaging, electroencephalography, and clinical data, have enhanced treatment outcome predictions; and (3) management, where AI has facilitated case identification, epidemiological research, health care burden assessment, and risk factor exploration for postherpetic neuralgia and other complications. …”
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The underlying molecular mechanisms and biomarkers of Hip fracture combined with deep vein thrombosis based on self sequencing bioinformatics analysis by Guanghua Shi, Xiaocui Shi, Meng Zhang, Rui Cheng, Mengqing Hu, Yu Zhao, Shimei Li, Xiuxiu Li, Haiyun Ma, Pengcui Li

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Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews by He-Li Xu, Ting-Ting Gong, Xin-Jian Song, Qian Chen, Qi Bao, Wei Yao, Meng-Meng Xie, Chen Li, Marcin Grzegorzek, Yu Shi, Hong-Zan Sun, Xiao-Han Li, Yu-Hong Zhao, Song Gao, Qi-Jun Wu

“…Diagnostic performance data were synthesized narratively. …”
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Artificial intelligence applications in pediatric ophthalmology: A comprehensive review by Anupama Janardhanan, Meenakshi Ravindran, Allapitchai Fathima, Neelam Pawar

“…The future of AI in pediatric ophthalmology appears bright, with ongoing research aimed at optimizing algorithms, personalizing treatment, and expanding telemedicine capabilities. …”
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