Review
Chemistry, Analytical
Ahmad Chaddad, Jihao Peng, Jian Xu, Ahmed Bouridane
Summary: Artificial intelligence with deep learning is widely used in medical imaging and healthcare tasks. To be a viable tool, AI needs to mimic human judgment and interpretation skills. Explainable AI aims to explain the information behind the black-box model of deep learning that reveals how decisions are made.
Article
Biochemical Research Methods
Lalith Kumar Shiyam Sundar, Otto Muzik, Irene Buvat, Luc Bidaut, Thomas Beyer
Summary: State-of-the-art patient management requires investigating both the anatomy and physiology of patients, with hybrid imaging techniques providing both structural and functional information. Artificial intelligence algorithms show promise in facilitating analysis of multi-parametric data in medical imaging, addressing challenges in extracting clinical information from large sets of multi-dimensional imaging data.
Article
Computer Science, Artificial Intelligence
Ali Raza, Kim Phuc Tran, Ludovic Koehl, Shujun Li
Summary: In this study, a novel end-to-end framework is proposed for ECG-based healthcare using explainable artificial intelligence and deep convolutional neural networks in a federated setting. The framework addresses challenges such as data availability and privacy concerns, and provides interpretability of the classification results, aiding clinical practitioners in decision-making.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Engineering, Chemical
Rayed AlGhamdi, Madini O. Alassafi, Abdulrahman A. Alshdadi, Mohamed M. Dessouky, Rabie A. Ramdan, Bassam W. Aboshosha
Summary: The Internet of Things (IoT) has become more pervasive in recent years, offering detailed description and interaction with the physical world. However, IoT systems have security vulnerabilities and lack privacy protection, making them susceptible to attacks. By combining blockchain technology with IoT, the healthcare industry can greatly improve efficiency, security, and transparency.
Editorial Material
Multidisciplinary Sciences
Guido C. H. E. de Croon
Summary: An autonomous drone has successfully competed and won against human drone-racing champions, thanks to advanced engineering and an artificial intelligence system that learns predominantly through trial and error.
Article
Computer Science, Information Systems
Deepti Saraswat, Pronaya Bhattacharya, Ashwin Verma, Vivek Kumar Prasad, Sudeep Tanwar, Gulshan Sharma, Pitshou N. Bokoro, Ravi Sharma
Summary: Healthcare 5.0 is a transformative shift in the healthcare domain, leveraging digital wellness and patient-centric technologies. Explainable AI (EXAI) enhances the transparency and interpretability of traditional AI models, improving clinical practices and predictive analysis in healthcare.
Editorial Material
Multidisciplinary Sciences
Ying-Lang Wang, Mao-Chih Huang
Summary: Engineers and algorithms have competed in a virtual test to design a step in the process of manufacturing computer chips. Pairing human expertise with computational efficiency proves most cost-effective, but only when the timing is right.
Article
Geosciences, Multidisciplinary
B. Balogh, D. Saint-Martin, A. Ribes
Summary: Researchers introduced a new toy model of atmospheric dynamics and found that traditional neural networks can lead to unstable trajectories in this model. Training on different learning samples based on Latin Hypercube Sampling can solve this issue and significantly influence the stability of dynamical systems driven by neural networks.
GEOPHYSICAL RESEARCH LETTERS
(2021)
Review
Computer Science, Interdisciplinary Applications
Shahab Shamshirband, Mahdis Fathi, Abdollah Dehzangi, Anthony Theodore Chronopoulos, Hamid Alinejad-Rokny
Summary: This paper investigates the application of deep learning approaches in healthcare systems, focusing on advanced network architectures, applications, and industry trends. The goal is to provide in-depth insights into the application of deep learning models in healthcare solutions, bridging deep learning techniques and human healthcare interpretability, and presenting existing challenges and future directions.
JOURNAL OF BIOMEDICAL INFORMATICS
(2021)
Article
Computer Science, Information Systems
Erzhena Tcydenova, Tae Woo Kim, Changhoon Lee, Jong Hyuk Park
Summary: This paper proposes an adversarial attack detection framework in machine learning-based intrusion detection systems, which detects adversarial attacks by explaining normal data records.
HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Bo Wang, Shuo Jin, Qingsen Yan, Haibo Xu, Chuan Luo, Lai Wei, Wei Zhao, Xuexue Hou, Wenshuo Ma, Zhengqing Xu, Zhuozhao Zheng, Wenbo Sun, Lan Lan, Wei Zhang, Xiangdong Mu, Chenxi Shi, Zhongxiao Wang, Jihae Lee, Zijian Jin, Minggui Lin, Hongbo Jin, Liang Zhang, Jun Guo, Benqi Zhao, Zhizhong Ren, Shuhao Wang, Wei Xu, Xinghuan Wang, Jianming Wang, Zheng You, Jiahong Dong
Summary: This paper presents the experience of building and deploying an AI system for rapid detection of COVID-19 pneumonia, which can save time for physicians and improve the performance of COVID-19 detection. The authors overcame various challenges in a interdisciplinary team and successfully deployed the system in four weeks.
APPLIED SOFT COMPUTING
(2021)
Review
Computer Science, Information Systems
Hadeer A. Helaly, Mahmoud Badawy, Amira Y. Haikal
Summary: This study reviews and analyzes current deep learning algorithms in healthcare systems, highlighting their contributions and limitations. By combining deep learning methods with the interpretability of human healthcare, it provides insights into deep learning applications in healthcare solutions.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Review
Ophthalmology
Cristina Gonzalez-Gonzalo, Eric F. Thee, Caroline C. W. Klaver, Aaron Y. Lee, Reinier O. Schlingemann, Adnan Tufail, Frank Verbraak, Clara Sanchez
Summary: This study focuses on the importance of trustworthy AI in ophthalmology and identifies the key aspects and challenges that need to be considered in the design pipeline to generate trustworthy AI systems. Stakeholders' roles and responsibilities are defined, and a collaborative approach is emphasized for the potential benefits of AI to be realized in real-world ophthalmic settings.
PROGRESS IN RETINAL AND EYE RESEARCH
(2022)
Article
Mathematics
Dhairya Jadav, Nilesh Kumar Jadav, Rajesh Gupta, Sudeep Tanwar, Osama Alfarraj, Amr Tolba, Maria Simona Raboaca, Verdes Marina
Summary: With the rise of smart devices in healthcare, data security has become a major concern. Blockchain and AI can be used to ensure the security of patient's wearable data and provide a secure and trusted framework for managing and sharing patient data.
Review
Computer Science, Information Systems
Monica Micucci, Antonio Iula
Summary: Machine learning methods are increasingly being applied in various fields, including ultrasound imaging, due to their effectiveness in solving challenging problems. This review focuses on the recent implementations of machine learning techniques in medical diagnostics and non-destructive evaluation in the field of ultrasound imaging. The studies were classified based on the human organ investigated and the methodology used, and solutions for detection/classification of material defects or patterns are discussed. The main merits of machine learning from the study analysis are summarized and discussed.
