Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda

Ferroelectric α-In2Se3 Semi-floating Gate Transistors for Multilevel Memory and Optoelectronic Logic Gate

Progress in artificial intelligence (AI) demands efficient data storage and high-speed processing. Traditional von Neumann architecture, with space separation of memory and computing units, struggles with increased data transmission, causing power inefficiency and date latency. To address this challenge, we designed a semi-floating gate transistor (SFGT) that integrates data storage and logical operation into a single device by employing a ferroelectric semiconductor α-In2Se3 as a semi-floating gate layer. Leveraging the ferroelectric polarization of α-In2Se3, the device exhibits improved non-volatile memory performance with a high program/erase ratio of 1 × 106 and reliable durability over 1000 cycles. Through the dual-gate modulation, the SFGT achieves multilevel storage function with at least seven controllable programming states and performs three types of digital logic gate operations ("AND", "NOR", and "OR") at an ultralow bias of 10 mV. Compared to traditional FGT architectures, the α-In2Se3-based semi-floating gate structure achieves multifunctional integration of data storage and logic computing, effectively addressing energy consumption and time delay issues in data transmission, making it highly significant for applications in data-intensive and low-power integrated circuits.

Artificial intelligence models for periodontitis classification: A systematic review

OBJECTIVES

The graded diagnosis of periodontitis has always been a difficulty for dentists. This systematic review aimed to investigate the performance of artificial intelligence (AI) models for periodontitis classification.

DATA

This review includes original studies that explore the application of AI in periodontitis classification systems.

SOURCES

Two reviewers independently conducted a comprehensive search of literature published up to April 2024 in databases including PubMed, Web of Science, MEDLINE, Scopus, and Cochrane Library.

STUDY SELECTION

A total of 28 articles were eventually included in this study, from which 10 mapping parameters were extracted and evaluated separately for each article.

RESULTS

AI's diagnostic capabilities are comparable to those of a general dentist/periodontist, achieving an overall diagnostic accuracy rate of over 70 % for periodontitis classification, with some reaching 80-90 %. Variations in diagnosis accuracy rates were observed across different stages of periodontitis.

CONCLUSIONS

The AI model provides a novel and relatively reliable method for periodontitis classification. However, several key issues remain to be addressed, including access to and quality of data, interpretation of the decision-making process of the model, the ability of the model to generalize, and ethical and privacy considerations.

CLINICAL SIGNIFICANCE

The development of AI models for periodontitis classification is expected to assist dentists in improving diagnostic efficiency and enhancing diagnostic accuracy, and further development is expected to assist telemedicine and home self-testing.

Digital leadership and employee innovative performance: the role of job crafting and person-job fit

With the development of the digital economy and digital technology, innovation-driven growth has become the key to the digital transformation of various organizations. Employee behavior and digital leadership affect the innovative performance of a company significantly. Using the proactive motivation model, this study constructed a moderated mediation model with job crafting as the mediating variable and person-job fit as the moderating variable. Through statistical analysis of 306 valid questionnaires answered by employees in manufacturing firms, this study determined how digital leadership affects innovative performance by promoting employees to carry out job crafting. The study conducted structure equation modeling to examine the hypotheses. The findings indicate the following: (1) Digital leadership has a positive effect on employee innovative performance. (2) Two of the three job crafting strategies (task crafting and cognitive crafting) mediate the relationship between digital leadership and employee innovation performance. (3) Person-job fit positively moderates the relationship between cognitive crafting and employee innovation performance. (4) Person-job fit positively moderates the indirect effect of digital leadership on employee innovation performance through cognitive crafting.

Artificial Intelligence in Gas Sensing: A Review

The role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in enhancing and automating gas sensing methods and the implications of these technologies for emergent gas sensor systems is reviewed. Applications of AI-based intelligent gas sensors include environmental monitoring, industrial safety, remote sensing, and medical diagnostics. AI, ML, and DL methods can process and interpret complex sensor data, allowing for improved accuracy, sensitivity, and selectivity, enabling rapid gas detection and quantitative concentration measurements based on sophisticated multiband, multispecies sensor systems. These methods can discern subtle patterns in sensor signals, allowing sensors to readily distinguish between gases with similar sensor signatures, enabling adaptable, cross-sensitive sensor systems for multigas detection under various environmental conditions. Integrating AI in gas sensor technology represents a paradigm shift, enabling sensors to achieve unprecedented performance, selectivity, and adaptability. This review describes gas sensor technologies and AI while highlighting approaches to AI-sensor integration.

Investigation of Pressure Injuries With Visual ChatGPT Integration: A Descriptive Cross-Sectional Study

AIM

This study aimed to assess the performance of Visual ChatGPT in staging pressure injuries using real patient images, compare it to manual staging by expert nurses, and evaluate its applicability as a supportive tool in wound care management.

DESIGN

This study used a descriptive and comparative cross-sectional design.

METHODS

The study analysed 155 patient pressure injury images from a hospital database, staged by expert nurses and Visual ChatGPT using the National Pressure Injury Advisory Panel guidelines. Visual ChatGPT's performance was tested in two scenarios: with images only and with images plus wound characteristics. Diagnostic performance was evaluated, including sensitivity, specificity, accuracy, and inter-rater agreement (Kappa).

RESULTS

Expert nurses demonstrated superior accuracy and specificity across most pressure injury stages. Visual ChatGPT performed comparably in early-stage pressure injuries, especially when wound characteristics were included, but struggled with unstageable and deep-tissue pressure injuries.

CONCLUSION

Visual ChatGPT shows potential as an artificial intelligence tool for pressure injury staging and wound management in nursing. However, improvements are necessary for complex cases, ensuring that artificial intelligence complements clinical judgement.

IMPLICATIONS FOR PROFESSION AND/OR PATIENT CARE

Visual ChatGPT can serve as an innovative artificial intelligence tool in clinical settings, assisting less experienced nurses and those in areas with limited wound care specialists in staging and managing pressure injuries.

REPORTING METHOD

The STROBE checklist was followed for reporting cross-sectional studies in line with the relevant EQUATOR guidelines.

PATIENT CONTRIBUTION

No patient or public contribution.

Antibiotics and Artificial Intelligence: Clinical Considerations on a Rapidly Evolving Landscape

The growing interest in leveraging artificial intelligence (AI) tools for healthcare decision-making extends to improving antibiotic prescribing. Large language models (LLMs), a type of AI trained on extensive datasets from diverse sources, can process and generate contextually relevant text. While their potential to enhance patient outcomes is significant, implementing LLM-based support for antibiotic prescribing is complex. Here, we specifically expand the discussion on this crucial topic by introducing three interconnected perspectives: (1) the distinctive commonalities, but also the crucial conceptual differences, between the use of LLMs as assistants in scientific writing and in supporting antibiotic prescribing in real-world practice; (2) the possibility and nuances of the expertise paradox; and (3) the peculiarities of the risk of error when considering LLMs to support complex tasks such as antibiotic prescribing.

A meta-analysis of AI and machine learning in project management: Optimizing vaccine development for emerging viral threats in biotechnology

OBJECTIVES

Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies across various industries, including healthcare, biotechnology, and vaccine development. These technologies offer immense potential to improve project management efficiency, decision-making, and resource utilization, especially in complex tasks such as vaccine development and healthcare innovations.

METHODS

A systematic meta-analysis was conducted by reviewing studies from databases like PubMed, IEEE Xplore, Scopus, Web of Science, EMBASE, and Google Scholar until September 2024. The analysis focused on the application of AI and ML in project management for vaccine development, biotechnology, and broader healthcare innovations using the PICO framework to guide study selection and inclusion. Statistical analyses were performed using Review Manager 5.4 and Comprehensive Meta-Analysis (CMA) software.

RESULTS

The meta-analysis reviewed 44 studies examining the integration of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare, biotechnology, and vaccine development project management. Results demonstrated significant improvements in efficiency, resource allocation, decision-making, and risk management. AI/ML applications notably accelerated vaccine development, from candidate identification to clinical trial optimization, and improved predictive modeling for efficacy and safety. Subgroup analysis revealed variations in effectiveness across healthcare sectors, with the highest pooled effect sizes observed in infectious disease control (1.2; 95 % CI: 0.85-1.50) compared to medical imaging (0.85; 95 % CI: 0.75-0.95). Studies employing AI techniques demonstrated a pooled effect size of 0.83 (95 % CI: 0.78-1.08). Despite the observed high heterogeneity (I2 = 99.04 %) and moderate-to-high risks of bias, sensitivity analyses confirmed the robustness of the findings. Overall, AI/ML integration offers transformative potential to enhance project management and vaccine development, driving innovation and efficiency in these critical fields.

CONCLUSION

AI and ML technologies show significant potential to transform project management practices in healthcare, biotechnology, and vaccine development by enhancing efficiency, predictive analytics, and decision-making capabilities. Their integration paves the way for more innovative, data-driven solutions that can adapt to evolving challenges in these fields.

Artificial intelligence in stroke risk assessment and management via retinal imaging

Retinal imaging, used for assessing stroke-related retinal changes, is a non-invasive and cost-effective method that can be enhanced by machine learning and deep learning algorithms, showing promise in early disease detection, severity grading, and prognostic evaluation in stroke patients. This review explores the role of artificial intelligence (AI) in stroke patient care, focusing on retinal imaging integration into clinical workflows. Retinal imaging has revealed several microvascular changes, including a decrease in the central retinal artery diameter and an increase in the central retinal vein diameter, both of which are associated with lacunar stroke and intracranial hemorrhage. Additionally, microvascular changes, such as arteriovenous nicking, increased vessel tortuosity, enhanced arteriolar light reflex, decreased retinal fractals, and thinning of retinal nerve fiber layer are also reported to be associated with higher stroke risk. AI models, such as Xception and EfficientNet, have demonstrated accuracy comparable to traditional stroke risk scoring systems in predicting stroke risk. For stroke diagnosis, models like Inception, ResNet, and VGG, alongside machine learning classifiers, have shown high efficacy in distinguishing stroke patients from healthy individuals using retinal imaging. Moreover, a random forest model effectively distinguished between ischemic and hemorrhagic stroke subtypes based on retinal features, showing superior predictive performance compared to traditional clinical characteristics. Additionally, a support vector machine model has achieved high classification accuracy in assessing pial collateral status. Despite this advancements, challenges such as the lack of standardized protocols for imaging modalities, hesitance in trusting AI-generated predictions, insufficient integration of retinal imaging data with electronic health records, the need for validation across diverse populations, and ethical and regulatory concerns persist. Future efforts must focus on validating AI models across diverse populations, ensuring algorithm transparency, and addressing ethical and regulatory issues to enable broader implementation. Overcoming these barriers will be essential for translating this technology into personalized stroke care and improving patient outcomes.

AI-driven identification of a novel malate structure from recycled lithium-ion batteries

The integration of Artificial Intelligence (AI) into the discovery of new materials offers significant potential for advancing sustainable technologies. This paper presents a novel approach leveraging AI-driven methodologies to identify a new malate structure derived from the treatment of spent lithium-ion batteries. By analysing bibliographic data and incorporating domain-specific knowledge, AI facilitated the identification and structure refinement of a new malate complex containing different metals (Ni, Mn, Co, and Cu). The synthesized compound was investigated through chemical and physical analyses, confirming its unique structure and composition. The present work proposes a significant difference from the classical use of AI in materials science, typically rooted in data-driven approaches relying on extensive datasets. This hybrid approach, combining AI's computational power with human expertise, not only expedited the structure determination process but also ensured the reliability and accuracy of the results. Finally, AI-driven material discovery highlights that waste materials can be transformed into valuable chemical products, suggesting their possible reuse, with several expected benefits, emphasising the role of AI in fostering not only innovation but also sustainability in material science.

A SEM-ANN analysis to examine impact of artificial intelligence technologies on sustainable performance of SMEs

This study investigates the impact of Artificial Intelligence (AI) adoption on the sustainable performance of small and medium-sized enterprises (SMEs). Employing a hybrid quantitative approach, this research combines Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANN) to examine the influence of various organizational, technological, and external factors on AI adoption. Key factors considered include top management support, employee capability, customer pressure, complexity, vendor support, and relative advantage. Data collected from 305 SMEs across multiple sectors were analyzed. The results reveal that all the proposed factors significantly and positively affect AI adoption, with top management support, employee capability, and relative advantage being the most influential predictors. Additionally, the adoption of AI technologies substantially enhances the economic, social, and environmental performance of SMEs, reflecting improvements in operational efficiency, cost reduction, and social value creation. The ANN results confirm the robustness of the SEM findings, highlighting the critical role of AI in driving sustainability outcomes. Furthermore, the study emphasizes the positive mediation effects of AI adoption on organizational performance, indicating that AI adoption serves as a key enabler in achieving both short-term operational gains and long-term sustainability objectives. This research contributes to the understanding of AI's transformative role in enhancing the sustainable performance of SMEs in developing economies, offering strategic insights for both policymakers and business leaders.

Harnessing the AI/ML in Drug and Biological Products Discovery and Development: The Regulatory Perspective

Artificial Intelligence (AI) has the disruptive potential to transform patients' lives via innovations in pharmaceutical sciences, drug development, clinical trials, and manufacturing. However, it presents significant challenges, ethical concerns, and risks across sectors and societies. AI's rapid advancement has revealed regulatory gaps as existing public policies struggle to keep pace with the challenges posed by these emerging technologies. The term AI itself has become commonplace to argue that greater "human oversight" for "machine intelligence" is needed to harness the power of this revolutionary technology for both potential and risk management, and hence to call for more practical regulatory guidelines, harmonized frameworks, and effective policies to ensure safety, scalability, data privacy, and governance, transparency, and equitable treatment. In this review paper, we employ a holistic multidisciplinary lens to survey the current regulatory landscape with a synopsis of the FDA workshop perspectives on the use of AI in drug and biological product development. We discuss the promises of responsible data-driven AI, challenges and related practices adopted to overcome limitations, and our practical reflections on regulatory oversight. Finally, the paper outlines a path forward and future opportunities for lawful ethical AI. This review highlights the importance of risk-based regulatory oversight, including diverging regulatory views in the field, in reaching a consensus.

Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives

The applications of artificial intelligence (AI) in pharmaceutical sectors have advanced drug discovery and development methods. AI has been applied in virtual drug design, molecule synthesis, advanced research, various screening methods, and decision-making processes. In the fourth industrial revolution, when medical discoveries are happening swiftly, AI technology is essential to reduce the costs, effort, and time in the pharmaceutical industry. Further, it will aid "genome-based medicine" and "drug discovery." AI may prepare proactive databases according to diseases, disorders, and appropriate usage of drugs which will facilitate the required data for the process of drug development. The application of AI has improved clinical trials on patient selection in a population, stratification, and sample assessment such as biomarkers, effectiveness measures, dosage selection, and trial length. Various studies suggest AI could be perform better compared to conventional techniques in drug discovery. The present review focused on the positive impact of AI in drug discovery and development processes in the pharmaceutical industry and beneficial usage in health sectors as well.

How do medical professionals make sense (or not) of AI? A social-media-based computational grounded theory study and an online survey

To investigate opinions and attitudes of medical professionals towards adopting AI-enabled healthcare technologies in their daily business, we used a mixed-methods approach. Study 1 employed a qualitative computational grounded theory approach analyzing 181 Reddit threads in the several subreddits of r/medicine. By utilizing an unsupervised machine learning clustering method, we identified three key themes: (1) consequences of AI, (2) physician-AI relationship, and (3) a proposed way forward. In particular Reddit posts related to the first two themes indicated that the medical professionals' fear of being replaced by AI and skepticism toward AI played a major role in the argumentations. Moreover, the results suggest that this fear is driven by little or moderate knowledge about AI. Posts related to the third theme focused on factual discussions about how AI and medicine have to be designed to become broadly adopted in health care. Study 2 quantitatively examined the relationship between the fear of AI, knowledge about AI, and medical professionals' intention to use AI-enabled technologies in more detail. Results based on a sample of 223 medical professionals who participated in the online survey revealed that the intention to use AI technologies increases with increasing knowledge about AI and that this effect is moderated by the fear of being replaced by AI.

Non-invasive multiple cancer screening using trained detection canines and artificial intelligence: a prospective double-blind study

The specificity and sensitivity of a simple non-invasive multi-cancer screening method in detecting breast, lung, prostate, and colorectal cancer in breath samples were evaluated in a double-blind study. Breath samples of 1386 participants (59.7% males, median age 56.0 years) who underwent screening for cancer using gold-standard screening methods, or a biopsy for a suspected malignancy were collected. The samples were analyzed using a bio-hybrid platform comprising trained detection canines and artificial intelligence tools. According to cancer screening/biopsy results, 1048 (75.6%) were negative for cancer and 338 (24.4%) were positive. Among the 338 positive samples, 261 (77.2%) were positive for one of the four cancer types that the bio-hybrid platform was trained to detect, with an overall sensitivity and specificity of 93.9% (95% confidence interval [CI] 90.3-96.2%) and 94.3% (95% CI 92.7%-95.5%), respectively. The sensitivity of each cancer type was similar; breast: 95.0% (95% CI 87.8-98.0%), lung: 95.0% (95% CI 87.8-98.0%), colorectal: 90.0% (95% CI 74.4-96.5%), prostate: 93.0% (95% CI 84.6-97.0%). The sensitivity of 14 other malignant tumors that the bio-hybrid platform was not trained to detect, but identified, was 81.8% (95% CI 71.8%-88.8%). Early cancer (0-2) detection sensitivity was 94.8% (95% CI 91.0%-97.1%). This bio-hybrid multi-cancer screening platform demonstrated high sensitivity and specificity and enables early-stage cancer detection.

The effect of the use of artificial intelligence in the preparation of patient education materials by nursing students on the understandability, actionability and quality of the material: A randomized controlled trial

AIM

This study was conducted to examine the effect of nursing students' use of artificial intelligence (AI) tools while preparing patient education materials on the understandability, actionability and quality of the material in terms of content.

BACKGROUND

AI can significantly improve nursing students' learning experiences, helping them to be better prepared for the challenges of a rapidly changing healthcare environment. By ensuring that materials are prepared in accordance with students' individual learning styles, preferences and needs, AI can both improve the effectiveness of educational materials and contribute to better learning outcomes.

DESIGN

This study was conducted as a randomized controlled experimental study.

METHODS

The study completed with 180 nursing students (control group = 89; intervention group = 91). The students in the control group used auxiliary tools such as books, journals and websites while preparing patient education materials. The students in the intervention group used AI tools in addition to tools such as books, journals and websites. Patient Education Materials Assessment Tool (PEMAT) and Global Quality Scale were used to evaluate the educational materials.

RESULTS

There are significant differences in students' PEMAT scores between the intervention and control groups in terms of both understandability, actionability and quality (p<0.001).

CONCLUSIONS

Nursing students' use of AI tools in preparing patient education materials has increased the understandability, actionability and quality of educational materials. The results show that the integration of AI into educational material preparation processes plays an important role in improving the effectiveness of educational contents.

ScholarGPT's performance in oral and maxillofacial surgery

OBJECTIVE

The purpose of this study is to evaluate the performance of Scholar GPT in answering technical questions in the field of oral and maxillofacial surgery and to conduct a comparative analysis with the results of a previous study that assessed the performance of ChatGPT.

MATERIALS AND METHODS

Scholar GPT was accessed via ChatGPT (www.chatgpt.com) on March 20, 2024. A total of 60 technical questions (15 each on impacted teeth, dental implants, temporomandibular joint disorders, and orthognathic surgery) from our previous study were used. Scholar GPT's responses were evaluated using a modified Global Quality Scale (GQS). The questions were randomized before scoring using an online randomizer (www.randomizer.org). A single researcher performed the evaluations at three different times, three weeks apart, with each evaluation preceded by a new randomization. In cases of score discrepancies, a fourth evaluation was conducted to determine the final score.

RESULTS

Scholar GPT performed well across all technical questions, with an average GQS score of 4.48 (SD=0.93). Comparatively, ChatGPT's average GQS score in previous study was 3.1 (SD=1.492). The Wilcoxon Signed-Rank Test indicated a statistically significant higher average score for Scholar GPT compared to ChatGPT (Mean Difference = 2.00, SE = 0.163, p < 0.001). The Kruskal-Wallis Test showed no statistically significant differences among the topic groups (χ² = 0.799, df = 3, p = 0.850, ε² = 0.0135).

CONCLUSION

Scholar GPT demonstrated a generally high performance in technical questions within oral and maxillofacial surgery and produced more consistent and higher-quality responses compared to ChatGPT. The findings suggest that GPT models based on academic databases can provide more accurate and reliable information. Additionally, developing a specialized GPT model for oral and maxillofacial surgery could ensure higher quality and consistency in artificial intelligence-generated information.

Intrusion detection using metaheuristic optimization within IoT/IIoT systems and software of autonomous vehicles

The integration of IoT systems into automotive vehicles has raised concerns associated with intrusion detection within these systems. Vehicles equipped with a controller area network (CAN) control several systems within a vehicle where disruptions in function can lead to significant malfunctions, injuries, and even loss of life. Detecting disruption is a primary concern as vehicles move to higher degrees of autonomy and the possibility of self-driving is explored. Tackling cyber-security challenges within CAN is essential to improve vehicle and road safety. Standard differences between different manufacturers make the implementation of a discreet system difficult; therefore, data-driven techniques are needed to tackle the ever-evolving landscape of cyber security within the automotive field. This paper examines the possibility of using machine learning classifiers to identify cyber assaults in CAN systems. To achieve applicability, we cover two classifiers: extreme gradient boost and K-nearest neighbor algorithms. However, as their performance hinges on proper parameter selection, a modified metaheuristic optimizer is introduced as well to tackle parameter optimization. The proposed approach is tested on a publicly available dataset with the best-performing models exceeding 89% accuracy. Optimizer outcomes have undergone rigorous statistical analysis, and the best-performing models were subjected to analysis using explainable artificial intelligence techniques to determine feature impacts on the best-performing model.

Perspectives toward the application of Artificial Intelligence in anesthesiology-related practices in Saudi Arabia: A cross-sectional study of physicians views

Background and Aims

The use of Artificial Intelligence (AI) relies on computer science and large datasets, with the technology mimicking human intelligence as it makes logical decisions. This study aims to assess the perceptions and experiences of anesthesiology practitioners toward AI and identify its benefits to healthcare professionals and patients, along with current and future applications of AI.

Methods

This cross-sectional descriptive online survey study was disseminated to physicians who work in anesthesiology practice in Saudi Arabia. Descriptive statistics were used to report the characteristics of the respondents and summarize the results of the survey.

Results

There were 109 responses, with 85.32% being male, 35.78% being aged 40-49 years, and 69.72% being consultant anesthesiologists. The majority of participants (73.39%) believed that AI could be used in multiple settings related to anesthesiology practice. Participants also believed that AI could facilitate access to data (76.15%), enable precise decision-making (75.23%), reduce medical errors (55.04%), reduce workload and shortage of healthcare personnel (53.21%), and allow healthcare personnel to focus on more demanding cases (69.72%). In addition, the majority of participants believed that AI can be beneficial to patients, in which 69.72% believed that AI can improve patient access to care, 77.06% believed that AI can facilitate patient education, and 65.14% believed that AI can guide patients during treatment. Lastly, 70.64% believed that AI would be beneficial to anesthesiology practices in the future. However, 61.47% claimed that their workplace has no plan for adopting AI.

Conclusions

The anesthesiologists showed generally positive attitudes towards AI, in spite of its limited utilization and implementation challenges. Strong beliefs exist about AI's future potential in anesthesia care and postgraduate education.

Artificial intelligence and learning environment: Human considerations

Background

Artificial intelligence (AI) has created new opportunities, challenges, and potentials in teaching; however, issues related to the philosophy of using AI technology in learners' learning have not been addressed and have caused some issues and concerns. This issue is due to the research gap in addressing issues related to ethical and human needs, and even values in AI in learning have become more obvious.

Objectives

This study investigates how human‐centered artificial intelligence (HAI) can help learners in a learning environment. In this regard, this article by developing key considerations of HAI in helping students tries to help implement or shift it in the future in learning environments.

Methods

To better understand the key considerations of HAI, qualitative methods and interview techniques were applied in this study. In this regard, 18 samples were interviewed from two groups of experts and faculty members in the fields of technology and computer science and social and humanities sciences. The thematic content analysis method was used to analyse qualitative data.

Results and Conclusions

The results show that AI attempts to integrate ethical and human values in the process of design, development, and research in the fields of recognising and dealing with negative emotions, targeted emotional nature, and access to fairness and justice. It also shows significant promise in understanding feelings and emotions in a learning environment.

Implications

Although AI has been studied in other contexts, HAI has not attracted much attention from researchers. Hence, this study has made worthwhile contributions to the literature as it has specifically focused on HAI in education. In addition, it can resolve some scientific community considerations regarding technological concerns in the field of AI. Furthermore, this article can increase social satisfaction with the use of AI by considering ethical considerations in the learning environment and can particularly benefit researchers, educators, and AI specialists who are involved in the study of HAI applications.

Artificial intelligence and prescription of antibiotic therapy: present and future

INTRODUCTION

In the past few years, the use of artificial intelligence in healthcare has grown exponentially. Prescription of antibiotics is not exempt from its rapid diffusion, and various machine learning (ML) techniques, from logistic regression to deep neural networks and large language models, have been explored in the literature to support decisions regarding antibiotic prescription.

AREAS COVERED

In this narrative review, we discuss promises and challenges of the application of ML-based clinical decision support systems (ML-CDSSs) for antibiotic prescription. A search was conducted in PubMed up to April 2024.

EXPERT OPINION

Prescribing antibiotics is a complex process involving various dynamic phases. In each of these phases, the support of ML-CDSSs has shown the potential, and also the actual ability in some studies, to favorably impacting relevant clinical outcomes. Nonetheless, before widely exploiting this massive potential, there are still crucial challenges ahead that are being intensively investigated, pertaining to the transparency of training data, the definition of the sufficient degree of prediction explanations when predictions are obtained through black box models, and the legal and ethical framework for decision responsibility whenever an antibiotic prescription is supported by ML-CDSSs.

Assessing AI adoption in developing country academia: A trust and privacy-augmented UTAUT framework

The rapid evolution of Artificial Intelligence (AI) and its widespread adoption have given rise to a critical need for understanding the underlying factors that shape users' behavioral intentions. Therefore, the main objective of this study is to explain user perceived behavioral intentions and use behavior of AI technologies for academic purposes in a developing country. This study has adopted the unified theory of acceptance and use of technology (UTAUT) model and extended it with two dimensions: trust and privacy. Data have been collected from 310 AI users including teachers, researchers, and students. This study finds that users' behavioral intention is positively and significantly associated with trust, social influence, effort expectancy, and performance expectancy. Privacy, on the other hand, has a negative yet significant relationship with behavioral intention unveiling that concerns over privacy can deter users from intending to use AI technologies which is a valuable insight for developers and educators. In determining use behavior, facilitating condition, behavioral intention, and privacy have significant positive impact. This study hasn't found any significant relationship between trust and use behavior elucidating that service providers should have unwavering focus on security measures, credible endorsements, and transparency to build user confidence. In an era dominated by the fourth industrial revolution, this research underscores the pivotal roles of trust and privacy in technology adoption. In addition, this study sheds light on users' perspective to effectively align AI-based technologies with the education system of developing countries. The practical implications encompass insights for service providers, educational institutions, and policymakers, facilitating the smooth adoption of AI technologies in developing countries while emphasizing the importance of trust, privacy, and ongoing refinement.

Artificial Intelligence for Climate Change Biology: From Data Collection to Predictions

In the era of big data, ecological research is experiencing a transformative shift, yet big-data advancements in thermal ecology and the study of animal responses to climate conditions remain limited. This review discusses how big data analytics and artificial intelligence (AI) can significantly enhance our understanding of microclimates and animal behaviors under changing climatic conditions. We explore AI's potential to refine microclimate models and analyze data from advanced sensors and camera technologies, which capture detailed, high-resolution information. This integration can allow researchers to dissect complex ecological and physiological processes with unprecedented precision. We describe how AI can enhance microclimate modeling through improved bias correction and downscaling techniques, providing more accurate estimates of the conditions that animals face under various climate scenarios. Additionally, we explore AI's capabilities in tracking animal responses to these conditions, particularly through innovative classification models that utilize sensors such as accelerometers and acoustic loggers. For example, the widespread usage of camera traps can benefit from AI-driven image classification models to accurately identify thermoregulatory responses, such as shade usage and panting. AI is therefore instrumental in monitoring how animals interact with their environments, offering vital insights into their adaptive behaviors. Finally, we discuss how these advanced data-driven approaches can inform and enhance conservation strategies. In particular, detailed mapping of microhabitats essential for species survival under adverse conditions can guide the design of climate-resilient conservation and restoration programs that prioritize habitat features crucial for biodiversity resilience. In conclusion, the convergence of AI, big data, and ecological science heralds a new era of precision conservation, essential for addressing the global environmental challenges of the 21st century.

Identification of lead inhibitors for 3CLpro of SARS-CoV-2 target using machine learning based virtual screening, ADMET analysis, molecular docking and molecular dynamics simulations

The SARS-CoV-2 3CLpro is a critical target for COVID-19 therapeutics due to its role in viral replication. We employed a screening pipeline to identify novel inhibitors by combining machine learning classification with similarity checks of approved medications. A voting classifier, integrating three machine learning classifiers, was used to filter a large database (∼10 million compounds) for potential inhibitors. This ensemble-based machine learning technique enhances overall performance and robustness compared to individual classifiers. From the screening, three compounds M1, M2 and M3 were selected for further analysis. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis compared these candidates to nirmatrelvir and azvudine. Molecular docking followed by 200 ns MD simulations showed that only M1 (6-[2,4-bis(dimethylamino)-6,8-dihydro-5H-pyrido[3,4-d]pyrimidine-7-carbonyl]-1H-pyrimidine-2,4-dione) remained stable. For azvudine and M1, the estimated median lethal doses are 1000 and 550 mg kg-1, respectively, with maximum tolerated doses of 0.289 and 0.614 log mg per kg per day. The predicted inhibitory activity of M1 is 7.35, similar to that of nirmatrelvir. The binding free energy based on Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) of M1 is -18.86 ± 4.38 kcal mol-1, indicating strong binding interactions. These findings suggest that M1 merits further investigation as a potential SARS-CoV-2 treatment.

Predictors of Health Care Practitioners' Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of Technology

BACKGROUND

Artificial intelligence-enabled clinical decision support systems (AI-CDSSs) offer potential for improving health care outcomes, but their adoption among health care practitioners remains limited.

OBJECTIVE

This meta-analysis identified predictors influencing health care practitioners' intention to use AI-CDSSs based on the Unified Theory of Acceptance and Use of Technology (UTAUT). Additional predictors were examined based on existing empirical evidence.

METHODS

The literature search using electronic databases, forward searches, conference programs, and personal correspondence yielded 7731 results, of which 17 (0.22%) studies met the inclusion criteria. Random-effects meta-analysis, relative weight analyses, and meta-analytic moderation and mediation analyses were used to examine the relationships between relevant predictor variables and the intention to use AI-CDSSs.

RESULTS

The meta-analysis results supported the application of the UTAUT to the context of the intention to use AI-CDSSs. The results showed that performance expectancy (r=0.66), effort expectancy (r=0.55), social influence (r=0.66), and facilitating conditions (r=0.66) were positively associated with the intention to use AI-CDSSs, in line with the predictions of the UTAUT. The meta-analysis further identified positive attitude (r=0.63), trust (r=0.73), anxiety (r=-0.41), perceived risk (r=-0.21), and innovativeness (r=0.54) as additional relevant predictors. Trust emerged as the most influential predictor overall. The results of the moderation analyses show that the relationship between social influence and use intention becomes weaker with increasing age. In addition, the relationship between effort expectancy and use intention was stronger for diagnostic AI-CDSSs than for devices that combined diagnostic and treatment recommendations. Finally, the relationship between facilitating conditions and use intention was mediated through performance and effort expectancy.

CONCLUSIONS

This meta-analysis contributes to the understanding of the predictors of intention to use AI-CDSSs based on an extended UTAUT model. More research is needed to substantiate the identified relationships and explain the observed variations in effect sizes by identifying relevant moderating factors. The research findings bear important implications for the design and implementation of training programs for health care practitioners to ease the adoption of AI-CDSSs into their practice.

Performance Metrics, Algorithms, and Applications of Artificial Intelligence in Vascular and Interventional Neurology: A Review of Basic Elements

Artificial intelligence (AI) is currently being used as a routine tool for day-to-day activity. Medicine is not an exception to the growing usage of AI in various scientific fields. Vascular and interventional neurology deal with diseases that require early diagnosis and appropriate intervention, which are crucial to saving patients' lives. In these settings, AI can be an extra pair of hands for physicians or in conditions where there is a shortage of clinical experts. In this article, the authors have reviewed the common metrics used in interpreting the performance of models and common algorithms used in this field.

Contributions of Artificial Intelligence to Analysis of Gut Microbiota in Autism Spectrum Disorder: A Systematic Review

BACKGROUND

Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental disorder whose etiology is not known today, but everything indicates that it is multifactorial. For example, genetic and epigenetic factors seem to be involved in the etiology of ASD. In recent years, there has been an increase in studies on the implications of gut microbiota (GM) on the behavior of children with ASD given that dysbiosis in GM may trigger the onset, development and progression of ASD through the microbiota-gut-brain axis. At the same time, significant progress has occurred in the development of artificial intelligence (AI).

METHODS

The aim of the present study was to perform a systematic review of articles using AI to analyze GM in individuals with ASD. In line with the PRISMA model, 12 articles using AI to analyze GM in ASD were selected.

RESULTS

Outcomes reveal that the majority of relevant studies on this topic have been conducted in China (33.3%) and Italy (25%), followed by the Netherlands (16.6%), Mexico (16.6%) and South Korea (8.3%).

CONCLUSIONS

The bacteria Bifidobacterium is the most relevant biomarker with regard to ASD. Although AI provides a very promising approach to data analysis, caution is needed to avoid the over-interpretation of preliminary findings. A first step must be taken to analyze GM in a representative general population and ASD samples in order to obtain a GM standard according to age, sex and country. Thus, more work is required to bridge the gap between AI in mental health research and clinical care in ASD.

Factors influencing pre-service special education teachers' intention toward AI in education: Digital literacy, teacher self-efficacy, perceived ease of use, and perceived usefulness

The use of artificial intelligence in education (AIEd) has become increasingly significant globally. In China, there is a lack of research examining the behavioral intention toward AIEd among pre-service special education (SPED) teachers in terms of digital literacy and teacher self-efficacy. Building on the technology acceptance model, our study evaluated the aspects influencing pre-service special education teachers' intention toward AI in education. Data was gathered from 274 pre-service SPED teachers studying at a Chinese public normal university of special education and analyzed using structural equation modeling (SEM). The results show that digital literacy is associated with the perceived usefulness and ease of use of AIEd, which influences SPED teachers' intention to use AIEd. Additionally, digital literacy significantly impacts the self-efficacy of SPED teachers. Given these results, AI designers in special education should comprehend the effectiveness and usability of AIEd for fostering behavioral intention formation. Simultaneously, special educational programs that identify key content and activities for digital literacy training should be developed, and educators should attempt to execute the relevant pre-service training to enhance the intention of pre-service SPED teachers toward AIEd.

AI Meets the Shopper: Psychosocial Factors in Ease of Use and Their Effect on E-Commerce Purchase Intention

The evolution of e-retail and the contribution of artificial intelligence in improving algorithms for greater customer engagement highlight the potential of these technologies to develop e-commerce further, making it more accessible and personalized to meet individual needs. This study aims to explore the psychosocial factors (subjective norms; faith; consciousness; perceived control) that affect AI-enabled ease of use and their impact on purchase intention in online retail. We will also assess the mediating effect of AI-enabled ease of use between psychosocial factors and consumer purchase intention. A quantitative methodology was used, and 1438 responses were collected from Portuguese consumers on e-retail. Structural equation modeling was used for the statistical treatment. The findings indicate that subjective norms do not positively impact AI-enabled ease of use, whereas factors such as faith, consciousness, and perceived control do enhance it. Furthermore, AI-enabled ease of use itself boosts purchase intention. Additionally, the effects of subjective norms, faith, consciousness, and perceived control on purchase intention are significantly enhanced when mediated by AI-enabled ease of use, highlighting the crucial role of usability in shaping consumer purchase behavior. The contribution of this study has been made through the formulation model that provides a systematized perspective about the influencers of purchase intentions and extends the knowledge about the impact of artificial intelligence in e-retail. Furthermore, this study offers insights into the impact of artificial intelligence in e-commerce-artificial intelligence directly affects purchase intentions and plays an important mediator role in the interaction mechanisms between psychosocial factors and purchase intentions.

Artificial intelligence empowering public health education: prospects and challenges

Artificial Intelligence (AI) is revolutionizing public health education through its capacity for intricate analysis of large-scale health datasets and the tailored dissemination of health-related information and interventions. This article conducts a profound exploration into the integration of AI within public health, accentuating its scientific foundations, prospective progress, and practical application scenarios. It underscores the transformative potential of AI in crafting individualized educational programs, developing sophisticated behavioral models, and informing the creation of health policies. The manuscript strives to thoroughly evaluate the extant landscape of AI applications in public health, scrutinizing critical challenges such as the propensity for data bias and the imperative of safeguarding privacy. By dissecting these issues, the article contributes to the conversation on how AI can be harnessed responsibly and effectively, ensuring that its application in public health education is both ethically grounded and equitable. The paper's significance is multifold: it aims to provide a blueprint for policy formulation, offer actionable insights for public health authorities, and catalyze the progression of health interventions toward increasingly sophisticated and precise approaches. Ultimately, this research anticipates fostering an environment where AI not only augments public health education but also does so with a steadfast commitment to the principles of justice and inclusivity, thereby elevating the standard and reach of health education initiatives globally.

Factors impacting the adoption of big data in healthcare: A systematic literature review

BACKGROUND

The term "big data" refers to the vast volume, variety, and velocity of data generated from various sources-e.g., sensors, social media, and online platforms. Big data adoption within healthcare poses an intriguing possibility for improving patients' health, increasing operational efficiency, and enabling data-driven decision-making. Despite considerable interest in the adoption of big data in healthcare, empirical research assessing the factors impacting the adoption process is lacking. Therefore, this review aimed to investigate the literature using a systematic approach to explore the factors that affect big data adoption in healthcare.

METHODS

A systematic literature review was conducted. The methodical and thorough process of discovering, assessing, and synthesizing relevant studies provided a full review of the available data. Several databases were used for the information search. Most of the articles retrieved from the search came from popular medical research databases, such as Scopus, Taylor & Francis, ScienceDirect, Emerald Insights, PubMed, Springer, IEEE, MDPI, Google Scholar, ProQuest Central, ProQuest Public Health Database, and MEDLINE.

RESULTS AND CONCLUSION

The results of the systematic literature review indicated that several theoretical frameworks (including the technology acceptance model; the technology, organization, and environment framework; the interactive communication technology adoption model; diffusion of innovation theory; dynamic capabilities theory; and the absorptive capability framework) can be used to analyze and understand technology acceptance in healthcare. It is vital to consider the safety of electronic health records during the use of big data. Furthermore, several elements were found to determine technological acceptance, including environmental, technological, organizational, political, and regulatory factors.

The effects of artificial intelligence on human resource activities and the roles of the human resource triad: opportunities and challenges

Introduction

This study analyzes the existing academic literature to identify the effects of artificial intelligence (AI) on human resource (HR) activities, highlighting both opportunities and associated challenges, and on the roles of employees, line managers, and HR professionals, collectively referred to as the HR triad.

Methods

We employed the scoping review method to capture and synthesize relevant academic literature in the AI-human resource management (HRM) field, examining 27 years of research (43 peer-reviewed articles are included).

Results

Based on the results, we propose an integrative framework that outlines the five primary effects of AI on HR activities: task automation, optimized HR data use, augmentation of human capabilities, work context redesign, and transformation of the social and relational aspects of work. We also detail the opportunities and challenges associated with each of these effects and the changes in the roles of the HR triad.

Discussion

This research contributes to the ongoing debate on AI-augmented HRM by discussing the theoretical contributions and managerial implications of our findings, along with avenues for future research. By considering the most recent studies on the topic, this scoping review sheds light on the effects of AI on the roles of the HR triad, enabling these key stakeholders to better prepare for this technological change. The findings can inform future academic research, organizations using or considering the application of AI in HRM, and policymakers. This is particularly timely, given the growing adoption of AI in HRM activities.

Bioinformatics and machine learning to support nanomaterial grouping

Nanomaterials (NMs) offer plenty of novel functionalities. Moreover, their physicochemical properties can be fine-tuned to meet the needs of specific applications, leading to virtually unlimited numbers of NM variants. Hence, efficient hazard and risk assessment strategies building on New Approach Methodologies (NAMs) become indispensable. Indeed, the design, the development and implementation of NAMs has been a major topic in a substantial number of research projects. One of the promising strategies that can help to deal with the high number of NMs variants is grouping and read-across. Based on demonstrated structural and physicochemical similarity, NMs can be grouped and assessed together. Within an established NM group, read-across may be performed to fill in data gaps for data-poor variants using existing data for NMs within the group. Establishing a group requires a sound justification, usually based on a grouping hypothesis that links specific physicochemical properties to well-defined hazard endpoints. However, for NMs these interrelationships are only beginning to be understood. The aim of this review is to demonstrate the power of bioinformatics with a specific focus on Machine Learning (ML) approaches to unravel the NM Modes-of-Action (MoA) and identify the properties that are relevant to specific hazards, in support of grouping strategies. This review emphasizes the following messages: 1) ML supports identification of the most relevant properties contributing to specific hazards; 2) ML supports analysis of large omics datasets and identification of MoA patterns in support of hypothesis formulation in grouping approaches; 3) omics approaches are useful for shifting away from consideration of single endpoints towards a more mechanistic understanding across multiple endpoints gained from one experiment; and 4) approaches from other fields of Artificial Intelligence (AI) like Natural Language Processing or image analysis may support automated extraction and interlinkage of information related to NM toxicity. Here, existing ML models for predicting NM toxicity and for analyzing omics data in support of NM grouping are reviewed. Various challenges related to building robust models in the field of nanotoxicology exist and are also discussed.

Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review

The recent advancements made in the realms of Artificial Intelligence (AI) and Artificial Intelligence of Things (AIoT) have unveiled transformative prospects and opportunities to enhance and optimize the environmental performance and efficiency of smart cities. These strides have, in turn, impacted smart eco-cities, catalyzing ongoing improvements and driving solutions to address complex environmental challenges. This aligns with the visionary concept of smarter eco-cities, an emerging paradigm of urbanism characterized by the seamless integration of advanced technologies and environmental strategies. However, there remains a significant gap in thoroughly understanding this new paradigm and the intricate spectrum of its multifaceted underlying dimensions. To bridge this gap, this study provides a comprehensive systematic review of the burgeoning landscape of smarter eco-cities and their leading-edge AI and AIoT solutions for environmental sustainability. To ensure thoroughness, the study employs a unified evidence synthesis framework integrating aggregative, configurative, and narrative synthesis approaches. At the core of this study lie these subsequent research inquiries: What are the foundational underpinnings of emerging smarter eco-cities, and how do they intricately interrelate, particularly urbanism paradigms, environmental solutions, and data-driven technologies? What are the key drivers and enablers propelling the materialization of smarter eco-cities? What are the primary AI and AIoT solutions that can be harnessed in the development of smarter eco-cities? In what ways do AI and AIoT technologies contribute to fostering environmental sustainability practices, and what potential benefits and opportunities do they offer for smarter eco-cities? What challenges and barriers arise in the implementation of AI and AIoT solutions for the development of smarter eco-cities? The findings significantly deepen and broaden our understanding of both the significant potential of AI and AIoT technologies to enhance sustainable urban development practices, as well as the formidable nature of the challenges they pose. Beyond theoretical enrichment, these findings offer invaluable insights and new perspectives poised to empower policymakers, practitioners, and researchers to advance the integration of eco-urbanism and AI- and AIoT-driven urbanism. Through an insightful exploration of the contemporary urban landscape and the identification of successfully applied AI and AIoT solutions, stakeholders gain the necessary groundwork for making well-informed decisions, implementing effective strategies, and designing policies that prioritize environmental well-being.

Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review

Heart failure (HF) is prevalent globally. It is a dynamic disease with varying definitions and classifications due to multiple pathophysiologies and etiologies. The diagnosis, clinical staging, and treatment of HF become complex and subjective, impacting patient prognosis and mortality. Technological advancements, like artificial intelligence (AI), have been significant roleplays in medicine and are increasingly used in cardiovascular medicine to transform drug discovery, clinical care, risk prediction, diagnosis, and treatment. Medical and surgical interventions specific to HF patients rely significantly on early identification of HF. Hospitalization and treatment costs for HF are high, with readmissions increasing the burden. AI can help improve diagnostic accuracy by recognizing patterns and using them in multiple areas of HF management. AI has shown promise in offering early detection and precise diagnoses with the help of ECG analysis, advanced cardiac imaging, leveraging biomarkers, and cardiopulmonary stress testing. However, its challenges include data access, model interpretability, ethical concerns, and generalizability across diverse populations. Despite these ongoing efforts to refine AI models, it suggests a promising future for HF diagnosis. After applying exclusion and inclusion criteria, we searched for data available on PubMed, Google Scholar, and the Cochrane Library and found 150 relevant papers. This review focuses on AI's significant contribution to HF diagnosis in recent years, drastically altering HF treatment and outcomes.

The influence of AI on the economic growth of different regions in China

High-quality development plays a crucial role in China's economic progress in the new era. It represents a new concept of advancement and mirrors the increasing aspirations of the populace for an improved standard of living. In this context, the role of artificial intelligence (AI) in promoting sustainable development cannot be overemphasized. This paper explores how AI technologies can drive the transition to a green, low-carbon and circular economy. We have established an index system to measure the development level of the artificial intelligence industry and the high-quality development of the economy, which is relevant to the current state of the artificial intelligence industry and the advancement of the economy. Panel data from 2008 to 2017 has been utilized for this purpose. Global principal component analysis method and entropy value method are employed in the evaluation. Through in-depth analysis of the application of artificial intelligence and environmental protection in various provinces and cities, we clarify that artificial intelligence promotes innovation, saves resources, and is conducive to the development of green economy in the new era.

Enhancing portfolio management using artificial intelligence: literature review

Building an investment portfolio is a problem that numerous researchers have addressed for many years. The key goal has always been to balance risk and reward by optimally allocating assets such as stocks, bonds, and cash. In general, the portfolio management process is based on three steps: planning, execution, and feedback, each of which has its objectives and methods to be employed. Starting from Markowitz's mean-variance portfolio theory, different frameworks have been widely accepted, which considerably renewed how asset allocation is being solved. Recent advances in artificial intelligence provide methodological and technological capabilities to solve highly complex problems, and investment portfolio is no exception. For this reason, the paper reviews the current state-of-the-art approaches by answering the core question of how artificial intelligence is transforming portfolio management steps. Moreover, as the use of artificial intelligence in finance is challenged by transparency, fairness and explainability requirements, the case study of post-hoc explanations for asset allocation is demonstrated. Finally, we discuss recent regulatory developments in the European investment business and highlight specific aspects of this business where explainable artificial intelligence could advance transparency of the investment process.

Effects of ChatGPT's AI capabilities and human-like traits on spreading information in work environments

The rapid proliferation and integration of AI chatbots in office environments, specifically the advanced AI model ChatGPT, prompts an examination of how its features and updates impact knowledge processes, satisfaction, and word-of-mouth (WOM) among office workers. This study investigates the determinants of WOM among office workers who are users of ChatGPT. We adopted a quantitative approach, utilizing a stratified random sampling technique to collect data from a diverse group of office workers experienced in using ChatGPT. The hypotheses were rigorously tested through Structural Equation Modeling (SEM) using the SmartPLS 4. The results revealed that system updates, memorability, and non-language barrier attributes of ChatGPT significantly enhanced knowledge acquisition and application. Additionally, the human-like personality traits of ChatGPT significantly increased both utilitarian value and satisfaction. Furthermore, the study showed that knowledge acquisition and application led to a significant increase in utilitarian value and satisfaction, which subsequently increased WOM. Age had a positive influence on WOM, while gender had no significant impact. The findings provide theoretical contributions by expanding our understanding of AI chatbots' role in knowledge processes, satisfaction, and WOM, particularly among office workers.

Artificial intelligence in wound care: diagnosis, assessment and treatment of hard-to-heal wounds: a narrative review

OBJECTIVE

The effective assessment of wounds, both acute and hard-to-heal, is an important component in the delivery by wound care practitioners of efficacious wound care for patients. Improved wound diagnosis, optimising wound treatment regimens, and enhanced prevention of wounds aid in providing patients with a better quality of life (QoL). There is significant potential for the use of artificial intelligence (AI) in health-related areas such as wound care. However, AI-based systems remain to be developed to a point where they can be used clinically to deliver high-quality wound care. We have carried out a narrative review of the development and use of AI in the diagnosis, assessment and treatment of hard-to-heal wounds. We retrieved 145 articles from several online databases and other online resources, and 81 of them were included in this narrative review. Our review shows that AI application in wound care offers benefits in the assessment/diagnosis, monitoring and treatment of acute and hard-to-heal wounds. As well as offering patients the potential of improved QoL, AI may also enable better use of healthcare resources.

Disruptive supply chain technology assessment for sustainability journey: A framework of probabilistic group decision making

The fourth industrial revolution, commonly recognized as Industry 4.0, has been ushered by modern and innovative intelligence and communication technologies. Concerns about disruptive technologies (DTs) are beginning to grow in developing countries, despite the fact that the trade-offs between implementation difficulties and realistic effects are still unknown. Hence, prioritization and promotion of such technologies should be considered when investing in them to ensure sustainability. The study aims to provide new critical insights into what DTs are and how to identify the significant DTs for sustainable supply chain (SSC). Understanding the DTs' potential for achieving holistic sustainability through effective technology adoption and diffusion is critical. To achieve the goal, an integrated approach combining the Bayesian method and the Best Worst Method (BWM) is utilized in this study to evaluate DTs in emerging economies' supply chain (SC). The systematic literature review yielded a total of 10 DTs for SSC, which were then evaluated using the Bayesian-BWM to explore the most critical DTs for a well-known example of the readymade garment (RMG) industry of Bangladesh. The results show that the three most essential DTs for SSC are "Internet of things (IoT)", "Cloud manufacturing", and "Artificial intelligence (AI)". The research insights will facilitate policymakers and practitioners in determining where to concentrate efforts during the technology adoption and diffusion stage in order to improve sustainable production through managing SC operations in an uncertain business environment.

Does AI-Driven Technostress Promote or Hinder Employees' Artificial Intelligence Adoption Intention? A Moderated Mediation Model of Affective Reactions and Technical Self-Efficacy

PURPOSE

The increasing integration of Artificial Intelligence (AI) within enterprises is generates significant technostress among employees, potentially influencing their intention to adopt AI. However, existing research on the psychological effects of this phenomenon remains inconclusive. Drawing on the Affective Events Theory (AET) and the Challenge-Hindrance Stressor Framework (CHSF), the current study aims to explore the "black box" between challenge and hindrance technology stressors and employees' intention to adopt AI, as well as the boundary conditions of this mediation relationship.

METHODS

The study employs a quantitative approach and utilizes three-wave data. Data were collected through the snowball sampling technique and a structured questionnaire survey. The sample comprises employees from 11 distinct organizations located in Guangdong Province, China. We received 301 valid questionnaires, representing an overall response rate of 75%. The theoretical model was tested through confirmatory factor analysis and regression analyses using Mplus and the Process macro for SPSS.

RESULTS

The results indicate that positive affect mediates the positive relationship between challenge technology stressors and AI adoption intention, whereas AI anxiety mediates the negative relationship between hindrance technology stressors and AI adoption intention. Furthermore, the results reveal that technical self-efficacy moderates the effects of challenge and hindrance technology stressors on affective reactions and the indirect effects of challenge and hindrance technology stressors on AI adoption intention through positive affect and AI anxiety, respectively.

CONCLUSION

Overall, our study suggests that AI-driven challenge technology stressors positively impact AI adoption intention through the cultivation of positive affect, while hindrance technology stressors impede AI adoption intention by triggering AI anxiety. Additionally, technical self-efficacy emerges as a crucial moderator in shaping these relationships. This research has the potential to make a meaningful contribution to the literature on AI adoption intention, deepening our holistic understanding of the influential mechanisms involved. Furthermore, the study affirms the applicability and relevance of Affective Events Theory (AET) and the Challenge-Hindrance Stressor Framework (CHSF). In practical terms, the research provides actionable insights for organizations to effectively manage employees' AI adoption intention.

How does artificial intelligence affect the transformation of China's green economic growth? An analysis from internal-structure perspective

Artificial intelligence (AI) has been proved to be an important engine of green economic development, yet how it will affect the internal structure of green economy is unknown. The aim of this study is to examine the impact and its mechanism of AI on green total factor productivity (GTFP) from the internal-structure perspective, by using provincial panel data of China from 2009 to 2021 and global Malmquist index. The main research results show that: (1) the development of AI contributes to China's GTFP growth. And this effect is more significant in undeveloped areas; (2) AI promotes China's GTFP growth mainly by improving resource allocation efficiency, while it exerts little impact through the paths of technological progress and scale efficiency; (3) the transmission mechanism of AI on GTFP varies greatly among China's three main regions. In the eastern region, AI improves GTFP mainly by both advancing technological progress and improving resource allocation efficiency, while in central region AI contributes to GTFP growth mainly through technological progress. Compared with the eastern and central regions, AI in the western region plays a stronger impact on GTFP through the channel of improving scale efficiency. This study helps to understand the pathways of artificial intelligence affecting the transformation of green economic growth and formulate differentiated regional policies in light of local conditions.

Revisiting the role of HR in the age of AI: bringing humans and machines closer together in the workplace

The functions of human resource management (HRM) have changed radically in the past 20 years due to market and technological forces, becoming more cross-functional and data-driven. In the age of AI, the role of HRM professionals in organizations continues to evolve. Artificial intelligence (AI) is transforming many HRM functions and practices throughout organizations creating system and process efficiencies, performing advanced data analysis, and contributing to the value creation process of the organization. A growing body of evidence highlights the benefits AI brings to the field of HRM. Despite the increased interest in AI-HRM scholarship, focus on human-AI interaction at work and AI-based technologies for HRM is limited and fragmented. Moreover, the lack of human considerations in HRM tech design and deployment can hamper AI digital transformation efforts. This paper provides a contemporary and forward-looking perspective to the strategic and human-centric role HRM plays within organizations as AI becomes more integrated in the workplace. Spanning three distinct phases of AI-HRM integration (technocratic, integrated, and fully-embedded), it examines the technical, human, and ethical challenges at each phase and provides suggestions on how to overcome them using a human-centric approach. Our paper highlights the importance of the evolving role of HRM in the AI-driven organization and provides a roadmap on how to bring humans and machines closer together in the workplace.

EBRC: Enhancing bioeconomy through research and communication

On September 12, 2022, President Biden issued Executive Order 14081 to enable the progress of biomanufacturing and biotechnology. This timely initiative will help overcome many challenging issues, and its potential impacts will be huge. This article discusses eight recommendations to make this US national initiative successful, encourage other nations to consider similar initiatives, and create a better world for the next generations.

A method of Mapping Process for scientific production using the Smart Bibliometrics

Big data launches a modern way of producing science and research around the world. Due to an explosion of data available in scientific databases, combined with recent advances in information technology, the researcher has at his disposal new methods and technologies that facilitate scientific development. Considering the challenges of producing science in a dynamic and complex scenario, the main objective of this article is to present a method aligned with tools recently developed to support scientific production, based on steps and technologies that will help researchers to materialize their objectives efficiently and effectively. Applying this method, the researcher can apply science mapping and bibliometric techniques with agility, taking advantage of an easy-to-use solution with cloud computing capabilities. From the application of the "Scientific Mapping Process", the researcher will be able to generate strategic information for a result-oriented scientific production, assertively going through the main steps of research and boosting scientific discovery in the most diverse fields of investigation. •The Scientific Mapping Process provides a method and a system to boost scientific development.•It automates Science Mapping and bibliometric analysis from scientific datasets.•It facilitates the researcher's work, increasing the assertiveness in scientific production.

The determinants of smart government systems adoption by public sector organizations in Saudi Arabia

This study investigates the determinants of smart government systems that are used in public service organizations in Saudi Arabia. The world's developed nations have conducted studies on smart government systems, but little research has been done on the Middle East, particularly in Saudi Arabia. This study fills the lacuna in the literature. Based on a number of theories including the Technology, Organization, and Environment framework (TOE), Unified Theory of Acceptance and Use of Technology (UTAUT), and Technology Acceptance Models (TAM), the study established an integrated conceptual research model. Online survey questionnaires were sent to 2060 employees in four ministries and after the second reminder a total of 427 completed answers were received, of which 419 (22% response rate) were deemed useable for the analysis. Multivariate statistical analysis was used to analyze the data and results indicated that 51% of the variance (R2 = 0.51) of employees' perceptions of smart government systems is explained by independent determinants. Findings show that security concerns (t (419) = 2.051, p < 0.041), ICT strategy (t (419) = 4.215, p < 0.000), managerial support (t (419) = 5.027, p < 0.000), incentives (t (419) = 5.263, p < 0.000), and trust (t (419) = -1.957, p < 0.050) are significant predictors of smart government systems acceptance. Meanwhile cultural values (t (419) = 0.669, p < 0.504) and religious values (t (419) = 1.082, p < 0.280) have no significant effect on the attitude to smart system adoption. Perception was found to have a strong significant effect on adoption of smart government systems (t (419) = 8.411, p < 0.000). These results have significant implications for the Saudi government's drive to implement smart government systems in all its agencies.

Evaluating the Use of Graph Neural Networks and Transfer Learning for Oral Bioavailability Prediction

Oral bioavailability is a pharmacokinetic property that plays an important role in drug discovery. Recently developed computational models involve the use of molecular descriptors, fingerprints, and conventional machine-learning models. However, determining the type of molecular descriptors requires domain expert knowledge and time for feature selection. With the emergence of the graph neural network (GNN), models can be trained to automatically extract features that they deem important. In this article, we exploited the automatic feature selection of GNN to predict oral bioavailability. To enhance the prediction performance of GNN, we utilized transfer learning by pre-training a model to predict solubility and obtained a final average accuracy of 0.797, an F1 score of 0.840, and an AUC-ROC of 0.867, which outperformed previous studies on predicting oral bioavailability with the same test data set.

AI as an Epistemic Technology

In this paper I argue that Artificial Intelligence and the many data science methods associated with it, such as machine learning and large language models, are first and foremost epistemic technologies. In order to establish this claim, I first argue that epistemic technologies can be conceptually and practically distinguished from other technologies in virtue of what they are designed for, what they do and how they do it. I then proceed to show that unlike other kinds of technology (including other epistemic technologies) AI can be uniquely positioned as an epistemic technology in that it is primarily designed, developed and deployed to be used in epistemic contexts such as inquiry, it is specifically designed, developed and deployed to manipulate epistemic content such as data, and it is designed, developed and deployed to do so particularly through epistemic operations such as prediction and analysis. As has been shown in recent work in the philosophy and ethics of AI (Alvarado, AI and Ethics, 2022a), understanding AI as an epistemic technology will also have significant implications for important debates regarding our relationship to AI technologies. This paper includes a brief overview of such implications, particularly those pertaining to explainability, opacity, trust and even epistemic harms related to AI technologies.

Regional unevenness in the construction of digital villages: A case study of China

In regard to the comprehensive promotion of rural revitalization, the construction of digital villages is a crucial development. Because the construction of digital villages is considerably novel, the existing studies mainly focus on the theoretical aspects pertaining to the rational and practical robustness of digital villages, and with regard to regional unevenness, the number of studies that consider the current characteristics, absolute gaps, and impact mechanisms pertaining to the construction of digital villages is insufficient. Based on the regional unevenness that characterizes digital village construction, this study proposes a research framework for digital technology-enabled village construction, which integrates three major factors, namely technology, institutions, and human resources; thus, the comprehensive assessment pertaining to the level of digital village construction is enhanced. This study, which applies the aforementioned research framework, constructs an index system for evaluating the construction level of digital villages, and to reveal the characteristics pertaining to regional heterogeneity and the main influencing factors pertaining to the construction level of digital villages in China (study period; 2015-2020), it utilizes the Dagum Gini coefficient method and the spatial econometric model. Consequently, the researchers observe the following: First, the level of digital village construction in China exhibits a "W-shaped" recovery growth. Second, with respect to the regional level, the eastern region exhibits the highest level of digital village construction, followed by central and western regions; furthermore, we observe that the eastern and western regions account for the greatest intra-regional variation, and that with regard to the overall difference, the inter-regional gap represents the main causative factor. Finally, with regard to influencing factors, technology and innovation capabilities, occupational differentiation of farmers, economic development significantly contribute to the level of digital village construction, whereas fiscal autonomy exerts a significant inhibiting effect. In regard to the level of digital village construction, the research framework and results may provide a novel analytical framework for examining the main sources of regional unevenness, and it may also provide a reference for decision-making, which can influence the construction of digital villages in China as well as in other countries.