The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency by Md. Faiyazuddin, Syed Jalal Q. Rahman, Gaurav Anand, Reyaz Kausar Siddiqui, Rachana Mehta, Mahalaqua Nazli Khatib, Shilpa Gaidhane, Quazi Syed Zahiruddin, Arif Hussain, Ranjit Sah

“…The study aims to describe AI in healthcare, including important technologies like robotics, machine learning (ML), deep learning (DL), and natural language processing (NLP), and to investigate how these technologies are used in patient interaction, predictive analytics, and remote monitoring. …”
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Predicting pediatric patient rehabilitation outcomes after spinal deformity surgery with artificial intelligence by Wenqi Shi, Felipe O. Giuste, Yuanda Zhu, Ben J. Tamo, Micky C. Nnamdi, Andrew Hornback, Ashley M. Carpenter, Coleman Hilton, Henry J. Iwinski, J. Michael Wattenbarger, May D. Wang

“…In total, 171 pre-operative clinical features are used to train six machine-learning models for post-operative outcomes prediction. …”
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New bitongling regulates gut microbiota to predict angiogenesis in rheumatoid arthritis via the gut-joint axis: a deep neural network approach by Yin Guan, Xiaoqian Zhao, Yun Lu, Yue Zhang, Yan Lu, Yue Wang

“…The study employed 16S ribosomal DNA (16S rDNA) sequencing to analyze gut microbiota composition, machine learning techniques to identify characteristic microbial taxa, and transcriptomic analysis (GSVA) to assess the impact on the VEGF signaling pathway. …”
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Cross-sectional design and protocol for Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI) by Gerald McGwin, Linda M Zangwill, Nicholas Evans, Shannon McWeeney, Cecilia S Lee, Bhavesh Patel, Jeffrey C Edberg, Cynthia Owsley, Aaron Lee, Cecilia Lee, Sally L Baxter, Michael Snyder, Samantha Hurst, Nicole Ehrhardt, Christopher Chute, Dawn S Matthies, Julia P Owen, Amir Bahmani, Sally Baxter, Edward Boyko, Aaron Cohen, Jorge Contreras, Garrison Cottrell, Virginia de Sa, Jeffrey Edberg, Irl Hirsch, Michelle Hribar, T.Y. Alvin Liu, Bonnie Maldenado, Sara Singer, Bradley Voytek, Joseph Yracheta, Linda Zangwill

“…Introduction Artificial Intelligence Ready and Equitable for Diabetes Insights (AI-READI) is a data collection project on type 2 diabetes mellitus (T2DM) to facilitate the widespread use of artificial intelligence and machine learning (AI/ML) approaches to study salutogenesis (transitioning from T2DM to health resilience). …”
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600 meters to VO2max: Predicting Cardiorespiratory Fitness with an Uphill Run by Kübra Stoican, Regina Oeschger

“…Recent advances in machine learning for maximal oxygen uptake (VO₂ max) prediction: A review. …”
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Vision-based manipulation of transparent plastic bags in industrial setups by F. Adetunji, F. Adetunji, A. Karukayil, A. Karukayil, P. Samant, P. Samant, S. Shabana, S. Shabana, F. Varghese, F. Varghese, U. Upadhyay, U. Upadhyay, R. A. Yadav, R. A. Yadav, A. Partridge, E. Pendleton, R. Plant, Y. R. Petillot, Y. R. Petillot, M. Koskinopoulou, M. Koskinopoulou

“…Integrating autonomous systems, including collaborative robots (cobots), into industrial workflows is crucial for improving efficiency and safety.MethodsThe proposed system employs advanced Machine Learning algorithms, particularly Convolutional Neural Networks (CNNs), for identifying transparent plastic bags under diverse lighting and background conditions. …”
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Research Progress of Intelligent Evaluation and Virtual Reality Based Training in Upper Limb Rehabilitation afrer Stroke by XIE Qiurong, LIN Wanqi, ZHANG Qi, SHENG Bo, ZHANG Yanxin, HUANG Jia

“…Automated assessment of upper extremity motor function based on machine learning algorithms with markerless sensing techniques has focused on the Fugl-Meyer assessment of upper extremity (FMA-UE), Brunnstrom stages, and Wolf motor function test (WMFT) scales and has been proved with high-scoring accuracy and time efficiency. …”
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Integration of single-cell transcriptomics and bulk transcriptomics to explore prognostic and immunotherapeutic characteristics of nucleotide metabolism in lung adenocarcinoma by Kai Zhang, Kai Zhang, Luyao Wang, Huili Chen, Lili Deng, Mengling Hu, Ziqiang Wang, Yiluo Xie, Chaoqun Lian, Xiaojing Wang, Xiaojing Wang, Jing Zhang

“…Genes associated with LUAD prognosis were identified using univariate COX analysis, and a prognostic risk model was constructed using the machine learning combination of Lasso + Stepcox. The model’s predictive validity was evaluated using KM survival and timeROC curves. …”
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Fiber-optic system for monitoring stability of quarry slopes by P. Sh. Madi, А. D. Аlkina, A. V. Yurchenko, A. D. Mekhtiyev, R. Zh. Aimagambetova

“…A hardware-software control complex has also been developed with a wide range of elements that allows you to adjust sensitivity and has machine learning elements.…”
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Exploring the assessment of post-cardiac valve surgery pulmonary complication risks through the integration of wearable continuous physiological and clinical data by Lixuan Li, Yuekong Hu, Zhicheng Yang, Zeruxin Luo, Jiachen Wang, Wenqing Wang, Xiaoli Liu, Yuqiang Wang, Yong Fan, Pengming Yu, Zhengbo Zhang

“…This study leverages wearable technology and machine learning algorithms to preoperatively identify high-risk individuals, thereby enhancing clinical decision-making for the mitigation of PPCs. …”
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Improving daily reference evapotranspiration forecasts: Designing AI-enabled recurrent neural networks based long short-term memory by Mumtaz Ali, Jesu Vedha Nayahi, Erfan Abdi, Mohammad Ali Ghorbani, Farzan Mohajeri, Aitazaz Ahsan Farooque, Salman Alamery

“…The main purpose of this investigation was to forecast the daily ETo trends at Melbourne and Sydney stations in Australia, where several cutting-edge machine learning methodologies were employed. The modeling approach encompassed the implementation of Neural Network (NN), Deep Learning (DL), Recurrent Neural Networks (RNN), RNN based Long Short-Term Memory (RNN-LSTM), and Convolutional Neural Network based LSTM (CNN-LSTM) to forecast daily ETo using historical meteorology data. …”
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Personalized prediction of glycemic responses to food in women with diet-treated gestational diabetes: the role of the gut microbiota by Polina V. Popova, Artem O. Isakov, Anastasiia N. Rusanova, Stanislav I. Sitkin, Anna D. Anopova, Elena A. Vasukova, Alexandra S. Tkachuk, Irina S. Nemikina, Elizaveta A. Stepanova, Angelina I. Eriskovskaya, Ekaterina A. Stepanova, Evgenii A. Pustozerov, Maria A. Kokina, Elena Y. Vasilieva, Lyudmila B. Vasilyeva, Soha Zgairy, Elad Rubin, Carmel Even, Sondra Turjeman, Tatiana M. Pervunina, Elena N. Grineva, Omry Koren, Evgeny V. Shlyakhto

“…The study involved 105 pregnant women (77 with GDM, 28 healthy), who underwent continuous glucose monitoring (CGM) for 7 days, provided food diaries, and gave stool samples for microbiome analysis. Machine learning models were created using CGM data, meal content, lifestyle factors, biochemical parameters, and microbiota data (16S rRNA gene sequence analysis). …”
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Predictive modelling and identification of critical variables of mortality risk in COVID-19 patients by Olawande Daramola, Tatenda Duncan Kavu, Maritha J. Kotze, Jeanine L. Marnewick, Oluwafemi A. Sarumi, Boniface Kabaso, Thomas Moser, Karl Stroetmann, Isaac Fwemba, Fisayo Daramola, Martha Nyirenda, Susan J. van Rensburg, Peter S. Nyasulu

“…Aside from clinical methods, artificial intelligence (AI)-based solutions such as machine learning (ML) models have been employed in treating COVID-19 cases. …”
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Hydra Radio Access Network (H-RAN): Multi-Functional Communications and Sensing Networks, Initial Access Implementation, and Task-2 Approach by Rafid I. Abd, Daniel J. Findley, Kwang Soon Kim

“…Furthermore, we employed a multi-sparse input and multi-task learning (SMTL) framework in the Hydra distributed unit (H-DU) artificial intelligence and machine learning (AI/ML) (AI/ML D-engine), where each task is tailored to be executed in a particular environment based on online feedback. …”
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Predictive role of SLC1A5 in neuroblastoma prognosis and immunotherapy by Jian Cheng, Miaomiao Sun, Xiao Dong, Yang Yang, Xiaohan Qin, Xing Zhou, Yongcheng Fu, Yuanyuan Wang, Jingyue Wang, Da Zhang

“…A prognostic signature, SRPS, was constructed and validated using machine-learning approaches. Immune infiltration analysis was performed to evaluate the tumor immune microenvironment. …”
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Editorial by Teddy Surya Gunawan

“…This issue also highlights the integration of AI and machine learning in optimizing engineering systems. From a fuzzy logic-based indoor navigation system to an energy management system for standalone microgrids, these innovations demonstrate AI's capability to enhance decision-making, reduce costs, and improve system reliability. …”
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Framework for smartphone-based grape detection and vineyard management using UAV-trained AI by Sergio Vélez, Mar Ariza-Sentís, Mario Triviño, Antonio Carlos Cob-Parro, Miquel Mila, João Valente

“…Recent technological and machine learning advancements, particularly in deep learning, have provided the tools necessary to create more efficient, automated processes that significantly reduce the time and effort required for these tasks. …”
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Multilevel determinants of racial/ethnic disparities in severe maternal morbidity and mortality in the context of the COVID-19 pandemic in the USA: protocol for a concurrent triang... by Bankole Olatosi, Jiajia Zhang, Xiaoming Li, Chen Liang, Jihong Liu, Peiyin Hung, Shan Qiao, Berry A Campbell, Myriam E Torres, Neset Hikmet

“…Non-Hispanic black and Hispanic pregnant women appear to have disproportionate SARS-CoV-2 infection and death rates.Methods and analysis We will use the socioecological framework and employ a concurrent triangulation, mixed-methods study design to achieve three specific aims: (1) examine the impacts of the COVID-19 pandemic on racial/ethnic disparities in severe maternal morbidity and mortality (SMMM); (2) explore how social contexts (eg, racial/ethnic residential segregation) have contributed to the widening of racial/ethnic disparities in SMMM during the pandemic and identify distinct mediating pathways through maternity care and mental health; and (3) determine the role of social contextual factors on racial/ethnic disparities in pregnancy-related morbidities using machine learning algorithms. We will leverage an existing South Carolina COVID-19 Cohort by creating a pregnancy cohort that links COVID-19 testing data, electronic health records (EHRs), vital records data, healthcare utilisation data and billing data for all births in South Carolina (SC) between 2018 and 2021 (>200 000 births). …”
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ANN-based two hidden layers computational procedure for analysis of heat transport dynamics in polymer-based trihybrid Carreau nanofluid flow over needle geometry by Adil Darvesh, Fethi Mohamed Maiz, Basma Souayeh, Luis Jaime Collantes Santisteban, Hakim AL. Garalleh, Afnan Al Agha, Lucerito Katherine Ortiz García, Nicole Anarella Sánchez-Miranda

“…Their advantages in handling nonlinearities and modeling high-dimensional data through integrating physical laws make them far superior to simpler machine learning and other traditional techniques, despite requiring greater data and computational resources. …”
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Blockchain-assisted improved interval type-2 fuzzy deep learning-based attack detection on internet of things driven consumer electronics by Rana Alabdan, Bayan Alabduallah, Nuha Alruwais, Munya A. Arasi, Somia A. Asklany, Omar Alghushairy, Fouad Shoie Alallah, Abdulrhman Alshareef

“…An intrusion detection system (IDS) is paramount in IoT security, as it dynamically monitors device behaviours and network traffic to detect and mitigate any possible cyber threats. Using machine learning (ML) methods and anomaly detection algorithms, IDS can rapidly identify abnormal activities, unauthorized access, or malicious behaviours within the IoT ecosystem, thus preserving the integrity of interconnected devices and networks, safeguarding sensitive data, and protecting against cyber-attacks. …”
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