Artificial Intelligence-based Technologies Research Articles

Implications of bacteria‒bacteria interactions within the plant microbiota for plant health and productivity.

Crop production currently relies on the widespread use of agrochemicals to ensure food security. This practice is considered unsustainable, yet has no viable alternative at present. The plant microbiota can fulfil various functions for its host, some of which could be the basis for developing sustainable protection and fertilization strategies for plants without relying on chemicals. To harness such functions, a detailed understanding of plant‒microbe and microbe‒microbe interactions is necessary. Among interactions within the plant microbiota, those between bacteria are the most common ones; they are not only of ecological importance but also essential for maintaining the health and productivity of the host plants. This review focuses on recent literature in this field and highlights various consequences of bacteria‒bacteria interactions under different agricultural settings. In addition, the molecular and genetic backgrounds of bacteria that facilitate such interactions are emphasized. Representative examples of commonly found bacterial metabolites with bioactive properties, as well as their modes of action, are given. Integrating our understanding of various binary interactions into complex models that encompass the entire microbiota will benefit future developments in agriculture and beyond, which could be further facilitated by artificial intelligence-based technologies.

Application of Artificial Intelligence-based Technology in College Archives Management

This study looks into the use of artificial intelligence (AI)-based technologies, notably Convolutional Neural Network (CNN) models and data augmentation approaches, in college archives management. With the rapid expansion of digital information, archive organizations have enormous issues in maintaining, organizing, and giving access to their vast collections of historical documents and scholarly resources. AI solutions present possible solutions to these difficulties by automating labor-intensive operations and improving the efficiency and accuracy of preservation workflows. In this study, they investigate the transformational potential of CNN models in digitization activities, utilizing their visual data processing skills to automate and enhance the digitization of visual resources within college archives. In addition, they look into how data augmentation techniques might improve the resilience and generalization capabilities of AI models, reducing biases and improving performance across a variety of archive collections. Using empirical research, theoretical analysis, and case studies, they present insights into the opportunities and obstacles of implementing AI-based technology in college archive management. Furthermore, they discuss ethical implications for the deployment of AI solutions, emphasizing the significance of transparency, accountability, and stakeholder participation. This study aims to inform archival practitioners, technologists, and stakeholders about the effective and responsible integration of AI technologies in cultural heritage preservation and promotion within academic institutions by elucidating the practical applications and implications of CNN models and data augmentation techniques in archival contexts.

PERLINDUNGAN TERHADAP PERKEMBANGAN LAYANAN KESEHATAN BERBASIS KECERDASAN BUATAN (ARTIFICIAL INTELLIGENCE) DI INDONESIA

Artificial Intelligence-based (AI) Health Service is a solution to the limited number and distribution of doctors in Indonesia. However, the use of AI technology also presents several challenges. This research used a normative juridical approach to analyse protection regarding the development of AI-based technology-based health services in Indonesia. The analysis shows that challenges to utilizing AI in healthcare include bias, protection of personal data and risk of malpractice. As a system that has no moral values, doctors is expected to be wiser in operating it. To ensure the delivery of quality AI-based health services, patients, doctors, service providers and the government need to understand and carry out their respective roles collaboratively.

Improving the effectiveness of screening for chronic noncommunicable diseases using artificial intelligence-based technologies

Improving the effectiveness of screening for chronic noncommunicable diseases using artificial intelligence-based technologies

Machine Learning Meets Cancer.

The role of machine learning (a part of artificial intelligence-AI) in the diagnosis and treatment of various types of oncology is steadily increasing. It is expected that the use of AI in oncology will speed up both diagnostic and treatment planning processes. This review describes recent applications of machine learning in oncology, including medical image analysis, treatment planning, patient survival prognosis, and the synthesis of drugs at the point of care. The fast and reliable analysis of medical images is of great importance in the case of fast-flowing forms of cancer. The introduction of ML for the analysis of constantly growing volumes of big data makes it possible to improve the quality of prescribed treatment and patient care. Thus, ML is expected to become an essential technology for medical specialists. The ML model has already improved prognostic prediction for patients compared to traditional staging algorithms. The direct synthesis of the necessary medical substances (small molecule mixtures) at the point of care could also seriously benefit from the application of ML. We further review the main trends in the use of artificial intelligence-based technologies in modern oncology. This review demonstrates the future prospects of using ML tools to make progress in cancer research, as well as in other areas of medicine. Despite growing interest in the use of modern computer technologies in medical practice, a number of unresolved ethical and legal problems remain. In this review, we also discuss the most relevant issues among them.

Who should decide how limited healthcare resources are prioritized? Autonomous technology as a compelling alternative to humans.

Who should decide how limited resources are prioritized? We ask this question in a healthcare context where patients must be prioritized according to their need and where advances in autonomous artificial intelligence-based technology offer a compelling alternative to decisions by humans. Qualitative (Study 1a; N = 50) and quantitative (Study 1b; N = 800) analysis identified agency, emotional experience, bias-free, and error-free as four main qualities describing people's perceptions of autonomous computer programs (ACPs) and human staff members (HSMs). Yet, the qualities were not perceived to be possessed equally by HSMs and ACPs. HSMs were endorsed with human qualities of agency and emotional experience, whereas ACPs were perceived as more capable than HSMs of bias- and error-free decision-making. Consequently, better than average (Study 2; N = 371), or relatively better (Studies 3, N = 181; & 4, N = 378), ACP performance, especially on qualities characteristic of ACPs, was sufficient to reverse preferences to favor ACPs over HSMs as the decision makers for how limited healthcare resources should be prioritized. Our findings serve a practical purpose regarding potential barriers to public acceptance of technology, and have theoretical value for our understanding of perceptions of autonomous technologies.

Artificial Intelligence as Author of Scientific Publications

Ascribing authorship of scientific publications to artificial intelligence is a complex and controversial issue. However, it is a challenging and uncertain problem that, given the growing development of artificial intelligence-based technologies that go beyond the performance of purely technical tasks and even contribute to the development of aspects such as the incorporation of scientific research information published in languages other than English, also contributing to potential insights in research, is becoming unavoidable when considering scientific publishing. This paper aims to add to this discussion by arguing that, although this is a challenging and even controversial position, it is inevitable and even ethically desirable to accept artificial intelligence, if it subsidizes sufficiently, as a (co-)author of any scientific publication. It is a matter of starting to think about how this attribution can be controlled and achieved with increasing respect for the ethics of scientific publication.

Economic Perspectives on Artificial Intelligence and the Digital Transformation of Education. A Case Study on Estonia

This paper delves into the integration of artificial intelligence within Estonia’s education system, showcasing the country’s global reputation for digital innovation. Utilising the method of the case study and the method of document analysis, we explore not only the practical outcomes, challenges, and strategies related to the adoption of artificial intelligence in the Estonian education system but also its economic implications. The research highlights artificial intelligence’s potential to lead to cost efficiencies and economic benefits for the education sector. Furthermore, we address the critical issue of fostering artificial intelligence literacy among students, schools, and universities, pinpointing data privacy and ethical considerations as key areas of concern. The strategies implemented by educational institutions to mitigate these challenges and promote a comprehensive understanding of artificial intelligence are also discussed. In conclusion, the paper underscores artificial intelligence’s transformative potential in reshaping education, offering guidance for other nations and academic entities aiming to successfully integrate artificial intelligence-based technologies. Emphasis is placed on the importance of a supportive policy framework and a solid ethical foundation to navigate the complexities of artificial intelligence adoption and ensure responsible use, while also considering the economic impact on the educational ecosystem and the labour market at large.

Novel Artificial Intelligence-Based Technology to Diagnose Asthma Using Methacholine Challenge Tests.

The methacholine challenge test (MCT) has high sensitivity but relatively low specificity for asthma diagnosis. This study aimed to develop and validate machine learning (ML) models to improve the diagnostic performance of MCT for asthma. Data from 1,501 patients with asthma symptoms who underwent MCT between 2015 and 2020 were analyzed. The patients were grouped as either the training (80%, n = 1,265) and test sets (20%, n = 236) depending on the time of referral. The conventional model (provocative concentration that causes a 20% decrease in forced expiratory volume in one second [FEV1]; PC20 ≤ 16 mg/mL) was compared with the prediction models derived from five ML methods: logistic regression, support vector machine, random forest, extreme gradient boosting, and artificial neural network. The area under the receiver operator characteristic curves (AUROC) and area under the precision-recall curves (AUPRC) of each model were compared. The prediction models were further analyzed using different input combinations of FEV1, forced vital capacity (FVC), and forced expiratory flow at 25%-75% of forced vital capacity (FEF25%-75%) values obtained during MCT. In total, 545 patients (36.3%) were diagnosed with asthma. The AUROC of the conventional model was 0.856 (95% confidence interval [CI], 0.852-0.861), and the AUPRC was 0.759 (95% CI, 0.751-0.766). All the five ML prediction models had higher AUROC and AUPRC values than those of the conventional model, and random forest showed both highest AUROC (0.950; 95% CI, 0.948-0.952) and AUROC (0.909; 95% CI, 0.905-0.914) when FEV1, FVC, and FEF25%-75% were included as inputs. Artificial intelligence-based models showed excellent performance in asthma prediction compared to using PC20 ≤ 16 mg/mL. The novel technology could be used to enhance the clinical diagnosis of asthma.

РОССИЙСКИЙ КОММЕРЧЕСКИЙ СЕКТОР: ПРИМЕНЕНИЕ ТЕХНОЛОГИЙ МЕНЕДЖМЕНТА, ОСНОВАННЫХ НА БАЗЕ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

This article examines the issue of the influence of artificial intelligence-based technologies in the activities of Russian commercial enterprises. Their high development potential becomes obvious, which justifies the need to continue this work. Despite the long-term nature of the use of such tools, the benefits of their implementation remain undeniable and allow the development of these technologies.

The Role of Artificial Intelligence-Based Technology with ChatGPT as an Educational Learning Media Innovation in Indonesia

ChatGPT with OpenAI is an artificial intelligence-based machine technology that is trained to imitate human conversation using NLP (Natural Language Processing) technology. ChatGPT can be used to produce quite scientific writing with good and effective formulation techniques. With the help of OpenAI technology, ChatGpt can help innovate in creating learning media and learning tools such as creating RPS (Semester Learning Plans) and writing textbooks for teaching staff, especially at the tertiary level. The aim of this research is to clarify the role of artificial intelligence in the world of education and this research method uses qualitative methods. The results of experiments carried out by researchers using ChatGPT produced + 693 words of writing, this number can still be developed further. The time speed only requires + 5-7 minutes to complete this experiment and includes time to document the processing results from ChatGPT.In conclusion, ChatGPT is useful, namely teaching, research and community service. So the presence of ChatGPT as a tool in creating scientific works, learning tools (RPS) and textbooks. Another benefit of ChatGpt, especially in developing the competencies (skills) of teaching staff (lecturers), can help carry out the Tri Dharma of Higher Education technology opens up opportunities to utilize this AI chatbot for education in Indonesia

Revolutionizing Vaccine Development for COVID-19: A Review of AI-Based Approaches

The evolvement of COVID-19 vaccines is rapidly being revolutionized using artificial intelligence-based technologies. Small compounds, peptides, and epitopes are collected to develop new therapeutics. These substances can also guide artificial intelligence-based modeling, screening, or creation. Machine learning techniques are used to leverage pre-existing data for COVID-19 drug detection and vaccine advancement, while artificial intelligence-based models are used for these purposes. Models based on artificial intelligence are used to evaluate and recognize the best candidate targets for future therapeutic development. Artificial intelligence-based strategies can be used to address issues with the safety and efficacy of COVID-19 vaccine candidates, as well as issues with manufacturing, storage, and logistics. Because antigenic peptides are effective at eliciting immune responses, artificial intelligence algorithms can assist in identifying the most promising COVID-19 vaccine candidates. Following COVID-19 vaccination, the first phase of the vaccine-induced immune response occurs when major histocompatibility complex (MHC) class II molecules (typically bind peptides of 12–25 amino acids) recognize antigenic peptides. Therefore, AI-based models are used to identify the best COVID-19 vaccine candidates and ensure the efficacy and safety of vaccine-induced immune responses. This study explores the use of artificial intelligence-based approaches to address logistics, manufacturing, storage, safety, and effectiveness issues associated with several COVID-19 vaccine candidates. Additionally, we will evaluate potential targets for next-generation treatments and examine the role that artificial intelligence-based models can play in identifying the most promising COVID-19 vaccine candidates, while also considering the effectiveness of antigenic peptides in triggering immune responses. The aim of this project is to gain insights into how artificial intelligence-based approaches could revolutionize the development of COVID-19 vaccines and how they can be leveraged to address challenges associated with vaccine development. In this work, we highlight potential barriers and solutions and focus on recent improvements in using artificial intelligence to produce COVID-19 drugs and vaccines, as well as the prospects for intelligent training in COVID-19 treatment discovery.

OP103 What To Include In A Health Technology Assessment Of Artificial Intelligence-Based Technologies: Results Of A Delphi Expert Survey

IntroductionClinicians are increasingly relying on artificial intelligence (AI) generated technologies for support in diagnosis, therapeutic decision-making, and prediction. Despite the increased focus on AI in health, an agreed HTA model for AI technologies, including consensus on new domains and topics to be assessed, is lacking.MethodsA Delphi survey was sent to a multidisciplinary expert panel asking about the importance of including the nine domains and associated topics presented in the EUnetHTA Core Model, as well as 20 additional topics identified through literature reviews, when assessing AI-supported health technologies. The Delphi survey was repeated twice among the same panelists and a nine-point Likert scale was used to identify the perceived relevance of each domain and topic.ResultsThe survey was sent to 87 various experts, with a total 47 of experts completing both Delphi rounds. The majority of panelists was knowledgeable of HTA (80%), familiar with the EUnetHTA Core Model (61%), and had adequate or high-level knowledge of AI (65%). The EUnetHTA domains most often indicated as “critical to include” were clinical effectiveness (82%), ethical aspects (81%), and cost effectiveness (77%), whereas organizational (59%) and social aspects (63%) were less often perceived as critical to assess. For the additional 20 topics identified through literature reviews, bias in data, accuracy in the AI model, appropriateness, and trustworthiness emerged as some of the new topics deemed critical to include in HTAs (all above 85%), whereas there was a lack of agreement on the relevance of including environmental (51%) and social sustainability (55%).ConclusionsThe study investigated in detail which issues should be included in an AI HTA core model. Current models need some adjustment and revision. At the same time, it is essential to open the discussion on including new domains and topics.

Applying AI in the Healthcare Sector: Difficulties

Artificial intelligence (AI) broadly speaking refers to any behavior shown by a computer or system that is similar to that of a person. Computers can learn from data without explicit human programming thanks to a kind of artificial intelligence known as "machine learning". The application of artificial intelligence (AI) technologies in medicine is one of the most important current trends in global healthcare. Artificial intelligence-based technologies are radically changing the global healthcare system by allowing for a drastic rebuilding of the medical diagnostics system and a corresponding decrease in healthcare costs. Prior to beginning treatment, an illness must be classified into which class of disorders it belongs. It is possible to classify the disease kind according to the feature space of the ailment. Machine learning algorithms can help with this problem.

Advancing sustainability in the food and nutrition system: a review of artificial intelligence applications.

Promoting sustainability in food and nutrition systems is essential to address the various challenges and trade-offs within the current food system. This imperative is guided by key principles and actionable steps, including enhancing productivity and efficiency, reducing waste, adopting sustainable agricultural practices, improving economic growth and livelihoods, and enhancing resilience at various levels. However, in order to change the current food consumption patterns of the world and move toward sustainable diets, as well as increase productivity in the food production chain, it is necessary to employ the findings and achievements of other sciences. These include the use of artificial intelligence-based technologies. Presented here is a narrative review of possible applications of artificial intelligence in the food production chain that could increase productivity and sustainability. In this study, the most significant roles that artificial intelligence can play in enhancing the productivity and sustainability of the food and nutrition system have been examined in terms of production, processing, distribution, and food consumption. The research revealed that artificial intelligence, a branch of computer science that uses intelligent machines to perform tasks that require human intelligence, can significantly contribute to sustainable food security. Patterns of production, transportation, supply chain, marketing, and food-related applications can all benefit from artificial intelligence. As this review of successful experiences indicates, artificial intelligence, machine learning, and big data are a boon to the goal of sustainable food security as they enable us to achieve our goals more efficiently.

Using AI-Based Technologies to Help Nurses Detect Behavioral Disorders: Narrative Literature Review.

The behavioral and psychological symptoms of dementia (BPSD) are common among people with dementia and have multiple negative consequences. Artificial intelligence-based technologies (AITs) have the potential to help nurses in the early prodromal detection of BPSD. Despite significant recent interest in the topic and the increasing number of available appropriate devices, little information is available on using AITs to help nurses striving to detect BPSD early. The aim of this study is to identify the number and characteristics of existing publications on introducing AITs to support nursing interventions to detect and manage BPSD early. A literature review of publications in the PubMed database referring to AITs and dementia was conducted in September 2023. A detailed analysis sought to identify the characteristics of these publications. The results were reported using a narrative approach. A total of 25 publications from 14 countries were identified, with most describing prospective observational studies. We identified three categories of publications on using AITs and they are (1) predicting behaviors and the stages and progression of dementia, (2) screening and assessing clinical symptoms, and (3) managing dementia and BPSD. Most of the publications referred to managing dementia and BPSD. Despite growing interest, most AITs currently in use are designed to support psychosocial approaches to treating and caring for existing clinical signs of BPSD. AITs thus remain undertested and underused for the early and real-time detection of BPSD. They could, nevertheless, provide nurses with accurate, reliable systems for assessing, monitoring, planning, and supporting safe therapeutic interventions.

Utilisation of Artificial Intelligence-based Technology for Agricultural Extension Services among Extension Professionals in Nigeria

The study examined the current awareness and usage, determined the level of utilisation of AI-based digital technology for agricultural extension services, and identified the merit and demerit of using AI-based digital technology for agricultural extension services. Data were collected through an online structured questionnaire from 131 extension professionals across Nigeria. Percentage and mean were used to describe and summarise the data. The findings revealed that 79.4% of the respondents were aware that AI-based digital technology can be used for agricultural extension services, while 55.7% reported that they had used the technology at one time or the other. About 45% of the respondents disseminated innovations and 34% demonstrated innovations and technologies through the use of AI-based digital technology. Also, 77.9% perceived reaching the target audience everywhere and every time as the major merit of AI- based digital technology while 71.8% identified high-cost implications of digital enablers as its major demerit. There was a high level of awareness but a low level of utilization of AI-based digital technology for agricultural extension services among agricultural extension professionals. On-the-job capacity building should be organized for the current professionals to promote the use of AIbased digital technology for agricultural extension services in Nigeria.

A comparative analysis of traditional and AI-based routing algorithms in electronic design automation

Rapid advances in artificial intelligence (AI) and machine learning have had a significant impact on various fields, including electronic design automation (EDA). This study aims to compare current EDA routing algorithms with AI-based routing algorithms, highlighting their respective advantages and limitations. Through a comprehensive analysis of existing EDA routing algorithms and artificial intelligence-based technologies, this study explores the applicability of these algorithms in different EDA scenarios, with a focus on their effectiveness and efficiency in routing tasks, and aims to compare and contrast traditional and AI-based routing algorithms in the context of electronic design automation. It also shows that the current AI technology will play an important role in the future development of EDA software. Therefore, the combination of AI technology may be the focus of EDA software development, so as to help elucidate the evolving landscape of EDA and provide insights into the potential future direction, and provide a a comprehensive understanding of their underlying methodologies, advantages, and limitations of traditional and AI-based algorithms in the context of electronic design automation.

A dataset of skin lesion images collected in Argentina for the evaluation of AI tools in this population

In recent years, numerous dermatological image databases have been published to make possible the development and validation of artificial intelligence-based technologies to support healthcare professionals in the diagnosis of skin diseases. However, the generation of these datasets confined to certain countries as well as the lack of demographic information accompanying the images, prevents having a real knowledge of in which populations these models could be used. Consequently, this hinders the translation of the models to the clinical setting. This has led the scientific community to encourage the detailed and transparent reporting of the databases used for artificial intelligence developments, as well as to promote the formation of genuinely international databases that can be representative of the world population. Through this work, we seek to provide details of the processing stages of the first public database of dermoscopy and clinical images created in a hospital in Argentina. The dataset comprises 1,616 images corresponding to 1,246 unique lesions collected from 623 patients.

Accuracy of Artificial Intelligence-Based Technologies for the Diagnosis of Atrial Fibrillation: A Systematic Review and Meta-Analysis.

Atrial fibrillation (AF) is the most common arrhythmia with a high burden of morbidity including impaired quality of life and increased risk of thromboembolism. Early detection and management of AF could prevent thromboembolic events. Artificial intelligence (AI)--based methods in healthcare are developing quickly and can be proved as valuable for the detection of atrial fibrillation. In this metanalysis, we aim to review the diagnostic accuracy of AI-based methods for the diagnosis of atrial fibrillation. A predetermined search strategy was applied on four databases, the PubMed on 31 August 2022, the Google Scholar and Cochrane Library on 3 September 2022, and the Embase on 15 October 2022. The identified studies were screened by two independent investigators. Studies assessing the diagnostic accuracy of AI-based devices for the detection of AF in adults against a gold standard were selected. Qualitative and quantitative synthesis to calculate the pooled sensitivity and specificity was performed, and the QUADAS-2 tool was used for the risk of bias and applicability assessment. We screened 14,770 studies, from which 31 were eligible and included. All were diagnostic accuracy studies with case-control or cohort design. The main technologies used were: (a) photoplethysmography (PPG) with pooled sensitivity 95.1% and specificity 96.2%, and (b) single-lead ECG with pooled sensitivity 92.3% and specificity 96.2%. In the PPG group, 0% to 43.2% of the tracings could not be classified using the AI algorithm as AF or not, and in the single-lead ECG group, this figure fluctuated between 0% and 38%. Our analysis showed that AI-based methods for the diagnosis of atrial fibrillation have high sensitivity and specificity for the detection of AF. Further studies should examine whether utilization of these methods could improve clinical outcomes.