Advancing a Hybrid Decision-Making Model in Anesthesiology: Applications of Artificial Intelligence in the Perioperative Setting

Focus issue: Artificial intelligence in medical physics.

Detecting Coronary Artery Calcium on Chest Radiographs: Can We Teach an Old Dog New Tricks?

We are glad to introduce you that the 2021 3rd International Conference on Artificial Intelligence Technologies and Applications (ICAITA 2021) was successfully held on September 10-12, 2021. In light of worldwide travel restriction and the impact of COVID-19, ICAITA 2021 was carried out in the form of virtual conference to avoid personnel gatherings. Because most participants were still highly enthusiastic about participating in this conference, we chose to carry out ICAITA 2021 via online platform according to the original schedule instead of postponing it.ICAITA 2021 is to bring together innovative academics and industrial experts in the field of Artificial Intelligence Technologies and Applications to a common forum. The primary goal of the conference is to promote research and developmental activities in Artificial Intelligence Technologies and Applications and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in Artificial Intelligence Technologies and Applications and related areas.This scientific event brings together more than 100 national and international researchers in artificial intelligence technologies and applications. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches.We were pleased to invite three distinguished experts to present their insightful speeches. Our first keynote speaker, Prof. Yau Kok Lim, from Sunway University, Malaysia. His research interests include Applied artificial intelligence, 5G networks, Cognitiveradio networks, Routing and clustering, Trust and reputation, Intelligent transportation system. And then we had Prof. Peter Sincak, from Technical University of Kosice, Slovakia. His research includes Artificial Intelligence and Intelligent Systems. Lastly, we were glad to invite Chinthaka Premachandra, from Shibaura Institute of Technology, Sri Lanka. His research interests include Artificial Intelligence, image processing and robotics. In the last part of the conference, all participants were invited to join in a WeChat group to discuss and explore the academic issues after the presentations. The online discussion was lasted for about 30-60 minutes. The first two parts were conducted via online collaboration tool, Zoom, while the online discussion was carried out through instant communication tool, WeChat. The online platform enabled all participants to join this grand academic event from their own home.We are glad to share with you that we still received lots of submissions from the conference during this special period. Hence, we selected a bunch of high-quality papers and compiled them into the proceedings after rigorously reviewed them. These papers feature following topics but are not limited to: Artificial Intelligence Applications & Technologies, Computing and the Mind, Foundations of Artificial Intelligence and other related topics. All the papers have been through rigorous review and process to meet the requirements of international publication standard.Lastly, we would like to express our sincere gratitude to the Chairman, the distinguished keynote speakers, as well as all the participants. We also want to thank the publisher for publishing the proceedings. May the readers could enjoy the gain some valuable knowledge from the proceedings. We are expecting more and more experts and scholars from all over the world to join this international event next year.The Committee of ICAITA 2021List of titles Committee member, General Conference Chair, Technical Program Committee Chair, Academic Committee Chair, Technical Program Committee Member, Academic Committee Member are available in this Pdf.

As we enter the era of "big data," an increasing amount of complex health-care data will become available. These data are often redundant, "noisy," and characterized by wide variability. In order to offer a precise and transversal view of a clinical scenario the artificial intelligence (AI) with machine learning (ML) algorithms and Artificial neuron networks (ANNs) process were adopted, with a promising wide diffusion in the near future. The present work aims to provide a comprehensive and critical overview of the current and potential applications of AI and ANNs in urology. A non-systematic review of the literature was performed by screening Medline, PubMed, the Cochrane Database, and Embase to detect pertinent studies regarding the application of AI and ANN in Urology. The main application of AI in urology is the field of genitourinary cancers. Focusing on prostate cancer, AI was applied for the prediction of prostate biopsy results. For bladder cancer, the prediction of recurrence-free probability and diagnostic evaluation were analysed with ML algorithms. For kidney and testis cancer, anecdotal experiences were reported for staging and prediction of diseases recurrence. More recently, AI has been applied in non-oncological diseases like stones and functional urology. AI technologies are growing their role in health care; but, up to now, their "real-life" implementation remains limited. However, in the near future, the potential of AI-driven era could change the clinical practice in Urology, improving overall patient outcomes.

The purpose of this study is to explore how artificial intelligence (AI) and big data can be used to solve the twin issues of athlete safety and sports event quality in a college sports environment. Furthermore, this study attempts to fill the literature vacuum regarding the application and effectiveness of artificial intelligence and big data in improving safety and quality in collegiate sports administration by investigating possible synergies between these elements and the implementation of developed technologies. This qualitative study used a sampling method to conduct in-depth interviews with 18 sports administrators and commentators. Using coding and classification methods, the data were evaluated thematically with a focus on artificial intelligence and big data applications. Research has found that artificial intelligence and big data play a key role in proactively reducing injuries, optimizing athlete performance and enabling data-driven decision-making. It also identifies barriers and opportunities for integrating these technologies, revealing their dynamic potential. This study provides new perspectives on the relationship between safety and quality and the application of artificial intelligence and big data in collegiate sports management. It also highlights the ways in which these technologies have transformative potential in sport. The findings have important implications for educational programs and policy development aimed at managing responsible technology integration and preparing future professionals in the field of sport management.

In the era of continuous development of computer technology, the application of artificial intelligence (AI) and big data is becoming more and more extensive. With the help of powerful computer and network technology, the art of visual communication (VISCOM) has ushered in a new chapter of digitalization and intelligence. How vision can better perform interdisciplinary and interdisciplinary artistic expression between art and technology and how to use more novel technology, richer forms, and more appropriate ways to express art has become a new problem in visual art creation. This essay aims to investigate and apply VISCOM art through big data and AI methods. This essay proposed the STING algorithm for big data for multi-resolution information clustering in VISCOM art. In addition, the convolutional neural network (CNN) in AI technology was used to identify the conveyed objects or scenes to achieve the purpose of designing art with different characteristics for different scenes and groups of people. STING is a multi-resolution clustering technique for big data, with the advantage of efficient data processing. In the experimental part, this essay selected a variety of design contents in VISCOM art, including logo design, text design, scene design, packaging design and poster design. STING and CNN algorithms were used to cluster and AI-identify the design elements 16 of the design projects might contain. The results showed that the overall average clustering accuracy was above 82%, the accuracy of scene element recognition mainly was above 80%, and the accuracy of facial recognition was above 80%; this showed that this essay applied AI and big data to the design of VISCOM, and had a good effect on the clustering and identification of design elements. According to expert scores, these applications' reliability and practicality scores were above 70 points, with an average of about 80 points. Therefore, applying big data and AI to VISCOM in this essay is reliable and feasible.

Opportunities and Challenges of Disruptive Innovation in Medicine Using Artificial Intelligence

Assessing perspectives on artificial intelligence applications to gastroenterology

Machine Learning (ML) algorithms, as the core technology in Artificial Intelligence (AI) applications, such as self-driving vehicles, make important decisions by performing a variety of data classification or prediction tasks. Attacks on data or algorithms in AI applications can lead to misclassification or misprediction, which can fail the applications. For each dataset separately, the parameters of ML algorithms should be tuned to reach a desirable classification or prediction accuracy. Typically, ML experts tune the parameters empirically, which can be time consuming and does not guarantee the optimal result. To this end, some research suggests an analytical approach to tune the ML parameters for maximum accuracy. However, none of the works consider the ML performance under attack in their tuning process. This paper proposes an analytical framework for tuning the ML parameters to be secure against attacks, while keeping its accuracy high. The framework finds the optimal set of parameters by defining a novel objective function, which takes into account the test results of both ML accuracy and its security against attacks. For validating the framework, an AI application is implemented to recognize whether a subject's eyes are open or closed, by applying k-Nearest Neighbors (kNN) algorithm on her Electroencephalogram (EEG) signals. In this application, the number of neighbors (k) and the distance metric type, as the two main parameters of kNN, are chosen for tuning. The input data perturbation attack, as one of the most common attacks on ML algorithms, is used for testing the security of the application. Exhaustive search approach is used to solve the optimization problem. The experiment results show k = 43 and cosine distance metric is the optimal configuration of kNN for the EEG dataset, which leads to 83.75% classification accuracy and reduces the attack success rate to 5.21%.

Enhancing Sustainability System Forecasts with Modern Artificial Intelligence (AI) Techniques: An Investigation in Beijing, China" is the title of the research that delves into the possibility of using cutting-edge AI methods to improve the accuracy of climate change models. The study's primary goal is to enhance the accuracy of climate predictions by using artificial intelligence techniques such as deep learning networks and machine learning algorithms, as conventional climate models fail to adequately represent complicated, non-linear climate systems. The research delves into the difficulties of predicting weather factors including temperature, precipitation, and air quality in the Beijing area, where pollution and fast urbanisation cause a great deal of climatic fluctuation. More precise risk assessments, enhanced decision-making for adaptation and mitigation plans, and enhanced modelling of future climatic scenarios are all possible outcomes of applying AI technologies to massive amounts of meteorological data. In light of Beijing's specific environmental circumstances, this study showcases the effective use of AI in climate research, showing how AI has the ability to transform predictive modelling and guide better climate policy. Global economic losses of more than $500 billion have been caused by climate change, which is already a significant hazard. It is harming both urban and natural systems. As AI draws on a wealth of online resources to provide timely recommendations grounded on reliable climate change forecasts, it has the potential to alleviate some of these problems. Energy efficiency, carbon sequestration and storage, transportation, grid management, building design, transportation, precision agriculture, industrial processes, reducing deforestation, resilient cities, and recent research and applications of artificial intelligence in climate change mitigation are highlighted in this review.

In recent years, Artificial Intelligence (AI) has shown transformative potential in advancing breast cancer care globally. This scoping review seeks to provide a comprehensive overview of AI applications in breast cancer care, examining how they could reshape diagnosis, treatment, and management on a worldwide scale and discussing both the benefits and challenges associated with their adoption. In accordance with PRISMA-ScR and ensuing guidelines on scoping reviews, PubMed, Web of Science, Cochrane Library, and Embase were systematically searched from inception to end of May 2024. Keywords included "Artificial Intelligence" and "Breast Cancer". Original studies were included based on their focus on AI applications in breast cancer care and narrative synthesis was employed for data extraction and interpretation, with the findings organized into coherent themes. Finally, 84 articles were included. The majority were conducted in developed countries (n = 54). The majority of publications were in the last 10 years (n = 83). The six main themes for AI applications were AI for breast cancer screening (n = 32), AI for image detection of nodal status (n = 7), AI-assisted histopathology (n = 8), AI in assessing post-neoadjuvant chemotherapy (NACT) response (n = 23), AI in breast cancer margin assessment (n = 5), and AI as a clinical decision support tool (n = 9). AI has been used as clinical decision support tools to augment treatment decisions for breast cancer and in multidisciplinary tumor board settings. Overall, AI applications demonstrated improved accuracy and efficiency; however, most articles did not report patient-centric clinical outcomes. AI applications in breast cancer care show promise in enhancing diagnostic accuracy and treatment planning. However, persistent challenges in AI adoption, such as data quality, algorithm transparency, and resource disparities, must be addressed to advance the field.

Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

Artificial intelligence: its applications in reproductive medicine and the assisted reproductive technologies

<b>Introduction:</b> A major application of Artificial Intelligence (AI) is to uncover relevant information from big data. This technology could play major roles in medicine, such as identification of new targets, discovery of new molecules, diagnostics, therapy selection, risk prediction and stratifying disease. <b>Aims and Objective:</b> To provide a review of existing algorithms for the application of AI in research and medical management of asthma. <b>Methods:</b> We performed a systematic review of English scientific articles, using the PubMed database, until Dec. 2018. Search terms included AI, machine learning, deep learning in single combination with asthma term. We included papers focused on human asthma, based on machine learning algorithms. <b>Results:</b> We selected 136 papers on 253 found after excluding duplicated and papers which did not meet inclusion criteria. 52 (40%) regarded the application of AI in asthma pathway analysis, phenotype and biomarker identification, 77 (56%) involved AI in asthma diagnosis, early prediction of exacerbations and predicting control, 7 (5%) are related to AI as support to the management and personalization of the treatment. <b>Conclusions:</b> Standard validation method of these technologies has not been established and data used in each work originate from different sources. Hence it is impossible to perform a direct outcome comparison of selected articles for each application. Evidences of AI confirmed proof of concept, but in order to transfer AI to clinical practice a systematic evaluation of properties, effects, and impacts of health technology is needed.

Rapid technological advances, particularly recent artificial intelligence (AI) revolutions such as generative AI (e.g., ChatGPT, DALL-E), digital assistants (e.g., Alexa, Siri), self-driving cars, and humanoid robots, have changed human lives and will continue to have even bigger impact on our future society. Some of those AI inventions already demonstrated potential for surpassing human intelligence and cognitive abilities; see, for example, the historic events of Watson (IBM’s supercomputer) and AlphaGo (Google DeepMind’s AI program) beating the human champions of Jeopardy and Go games, respectively. At the same time, there were new revelations of AI limitations and biases. Thus, AI advances raise challenging questions for education. Will AI replace human intelligence and learning? What are the new models of education toward effective and ethical uses of AI? What are the key enablers of the AI revolution, such as big data and machine learning? What applications of AI may help improve the quality of education? Answering these critical questions requires interdisciplinary research that cuts across the fields of education psychology, learning science, AI/data science, and technology. Because it is an emerging field of research, the literature is in flux and expanding. Thus, our goal here is to give readers a quick introduction to this broad topic by drawing upon a short selection of readings. This entry is organized into three major sections, where we present commentaries along with a list of annotated references on each of the following areas: (1) AI Impacts on the Society and Education; (2) AI Enablers: Big Data in Education and Machine Learning; and (3) Applications of AI in Education: Examples and Evidence.