TRACE Model: Predicting Treatment Response to Transarterial Chemoembolization in Unresectable Hepatocellular Carcinoma by Wang W, Zhang Q, Cui Y, Zhang S, Li B, Xia T, Song Y, Zhou S, Ye F, Xiao W, Cao K, Chi Y, Qu J, Zhou G, Chen Z, Zhang T, Chen X, Ju S, Wang YC

“…Weilang Wang,1,* Qi Zhang,2,* Ying Cui,1,* Shuhang Zhang,1 Binrong Li,1 Tianyi Xia,1 Yang Song,3 Shuwei Zhou,1 Feng Ye,4 Wenbo Xiao,5 Kun Cao,6 Yuan Chi,7 Jinrong Qu,8 Guofeng Zhou,9,10 Zhao Chen,11 Teng Zhang,12 Xunjun Chen,13 Shenghong Ju,1 Yuan-Cheng Wang1 1Department of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, Nanjing, 210009, People’s Republic of China; 2Center of Interventional Radiology and Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, People’s Republic of China; 3MR Scientific Marketing, Siemens Healthineers Ltd, Shanghai, People’s Republic of China; 4Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China; 5Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, People’s Republic of China; 6Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, People’s Republic of China; 7Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People’s Republic of China; 8Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan, People’s Republic of China; 9Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China; 10Shanghai Institute of Medical Imaging, Shanghai, People’s Republic of China; 11Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 12Institute for Artificial Intelligence in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, People’s Republic of China; 13Department of Radiology, The People’s Hospital of Xuyi County, Huaian, Jiangsu, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yuan-Cheng Wang, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, Jiangsu, 210009, People’s Republic of China, Tel +086 25 83272121, Fax +086 25 83311083, Email yuancheng_wang@seu.edu.cnPurpose: To develop and validate a predictive model for predicting six-month outcome by integrating pretreatment MRI features and one-month treatment response after TACE.Methods: A total of 108 patients with 160 hCCs from a single-arm, multicenter clinical trial (NCT03113955) were analyzed and served as the training cohort. …”
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Explainable and perturbation-resilient model for cyber-threat detection in industrial control systems Networks by Urslla Uchechi Izuazu, Cosmas Ifeanyi Nwakanma, Dong-Seong Kim, Jae Min Lee

“…XC-TDF enhances robustness against noise and adversarial attacks using regularization and adversarial training respectively, and also improves transparency through an eXplainable Artificial Intelligence (XAI) module. Simulation results demonstrate its effectiveness, showing resilience to perturbation by achieving commendable accuracy of 100% and 99.4% on the Wustl-IIoT2021 and Edge-IIoT datasets, respectively.…”
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A generic self-learning emotional framework for machines by Alberto Hernández-Marcos, Eduardo Ros

“…Here we propose that emotions correspond to distinct temporal patterns perceived in crucial values for living beings in their environment (like recent rewards, expected future rewards or anticipated world states) and introduce a fully self-learning emotional framework for Artificial Intelligence agents convincingly associating them with documented natural emotions. …”
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Digitalization and Inclusive Education: Common Ground by D. Z. Akhmetova, T. S. Artyukhina, M. R. Bikbayeva, I. A. Sakhnova, M. A. Suchkov, E. A. Zaytseva

“…In order to do this, the entire process of digitalization and the use of artificial intelligence must be mastered. The main thing is to remember that “person” should be in the center of attention during the process of digitalization of socio-political processes.…”
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A scoping review of robustness concepts for machine learning in healthcare by Alan Balendran, Céline Beji, Florie Bouvier, Ottavio Khalifa, Theodoros Evgeniou, Philippe Ravaud, Raphaël Porcher

“…Abstract While machine learning (ML)-based solutions—often referred to as artificial intelligence (AI) solutions—have demonstrated comparable or superior performance to human experts across various healthcare applications, their vulnerability to perturbations and stability to variations due to new environments—essentially, their robustness—remains ambiguous and often overlooked. …”
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When Healthcare Professionals Use AI: Exploring Work Well-Being Through Psychological Needs Satisfaction and Job Complexity by Weiwei Huo, Qiuchi Li, Bingqian Liang, Yixin Wang, Xuanlei Li

“…This study examines how the use of artificial intelligence (AI) by healthcare professionals affects their work well-being through the satisfaction of basic psychological needs, framed within Self-Determination Theory. …”
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Enhancing adversarial transferability with local transformation by Yang Zhang, Jinbang Hong, Qing Bai, Haifeng Liang, Peican Zhu, Qun Song

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AI-based medical ethics education: examining the potential of large language models as a tool for virtue cultivation by Shimpei Okamoto, Masanori Kataoka, Makoto Itano, Tsutomu Sawai

“…Abstract Background With artificial intelligence (AI) increasingly revolutionising medicine, this study critically evaluates the integration of large language models (LLMs), known for advanced text processing and generation capabilities, in medical ethics education, focusing on promoting virtue. …”
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An optimized ensemble grey wolf-based pipeline for monkeypox diagnosis by Ahmed I. Saleh, Asmaa H. Rabie, Shimaa E. ElSayyad, Ali E. Takieldeen, Fahmi Khalifa

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CFD Analysis for Aerodynamic Performance Optimization of a Vertical Axis Helical Wind Turbine Using Rigid PVC by Outar Hamza, Ennadafy Hamza, Jammoukh Mustapha, El Khaili Mohamed

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Prediction of buckling damage of steel equal angle structural members using hybrid machine learning techniques by Nang Xuan Ho, Tien-Thinh Le, The-Hung Dinh, Van-Hai Nguyen

“…In particular, a hybrid Artificial Intelligence (AI)-based model involving Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) was developed and calibrated for the problem at hand. …”
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New colleague or gimmick hurdle? A user-centric scoping review of the barriers and facilitators of robots in hospitals. by Mathias Kofoed Rasmussen, Anna Schneider-Kamp, Tobias Hyrup, Alessandro Godono

“…To address this escalating demand for healthcare services, healthcare systems are increasingly adopting robotic technology and artificial intelligence (AI), which promise to optimise costs, improve working conditions, and increase the quality of care. …”
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New Trends in Robotics for Agriculture: Integration and Assessment of a Real Fleet of Robots by Luis Emmi, Mariano Gonzalez-de-Soto, Gonzalo Pajares, Pablo Gonzalez-de-Santos

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Explainable AI chatbots towards XAI ChatGPT: A review by Attila Kovari

“…Advances in artificial intelligence (AI) have had a major impact on natural language processing (NLP), even more so with the emergence of large-scale language models like ChatGPT. …”
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University proceedings. Volga region. Social sciences by V.G. Romanovskiy

“…The article formulates general conclusions about the continued trend of expanding digital crime, the emergence of new subjects of criminal law (Artificial intelligence, an-thropoid robots), the development of digital criminology, and the transformation of the principles of criminal law. …”

Updates on CAD risk assessment: using the coronary artery calcium score in combination with traditional risk factors by Kiara Rezaie-Kalamtari, Zeinab Norouzi, Alireza Salmanipour, Hossein Mehrali

“…According to the results of the CAC the patients may be candidate for further evaluation for needing revascularization, medical treatment, or routine follow-up. Adding artificial intelligence (AI) to CAC will prepare more data and can increase the reliability of our approach to the patients promising a bright future to improve this technology.…”
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Trust in AI: progress, challenges, and future directions by Saleh Afroogh, Ali Akbari, Emmie Malone, Mohammadali Kargar, Hananeh Alambeigi

“…Abstract The increasing use of artificial intelligence (AI) systems in our daily lives through various applications, services, and products highlights the significance of trust and distrust in AI from a user perspective. …”
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Advanced decision analytics with fuzzy logic integrating AI and computational thinking for personnel selection by Mudiarasan Kuppusamy, Jugindar Singh, Priya Raman, Neda Abdolrahimi

“…Advanced decision analytics, fuzzy logic, artificial intelligence, and computational thinking may be used to improve personnel selection in today's dynamic industry. …”
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A knowledge graph for crop diseases and pests in China by Rongen Yan, Ping An, Xianghao Meng, Yakun Li, Dongmei Li, Fu Xu, Depeng Dang

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