Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

Cognitive and behavioral markers for human detection error in AI-assisted bridge inspection

Integrating Artificial Intelligence (AI) and drone technology into bridge inspections offers numerous advantages, including increased efficiency and enhanced safety. However, it is essential to recognize that this integration changes the cognitive ergonomics of the inspection task. Gaining a deeper understanding of how humans process information and behave when collaborating with drones and AI systems is necessary for designing and implementing effective AI-assisted inspection drones. To further understand human-drone-AI intricate dynamics, an experiment was conducted in which participants' biometric and behavioral data were collected during a simulated drone-enabled bridge inspection under two conditions: with an 80% accurate AI assistance and with no AI assistance. Results indicate that cognitive and behavioral factors, including vigilance, cognitive processing intensity, gaze patterns, and visual scanning efficiency can influence inspectors' performance respectively in either condition. This highlights the importance of designing inspection protocols, drones and AI systems based on a comprehensive understanding of the cognitive processes required in each condition to prevent cognitive overload and minimize errors. We also remark on the visual scanning and gaze patterns associated with a higher chance of missing critical information in each condition, insights that inspectors can use to enhance their inspection performance.

Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping Review

BACKGROUND

The rapid advancement of digital technologies, particularly in big data analytics (BDA), artificial intelligence (AI), machine learning (ML), and deep learning (DL), is reshaping the global health care system, including in Bangladesh. The increased adoption of these technologies in health care delivery within Bangladesh has sparked their integration into health care and public health research, resulting in a noticeable surge in related studies. However, a critical gap exists, as there is a lack of comprehensive evidence regarding the research landscape; regulatory challenges; use cases; and the application and adoption of BDA, AI, ML, and DL in the health care system of Bangladesh. This gap impedes the attainment of optimal results. As Bangladesh is a leading implementer of digital technologies, bridging this gap is urgent for the effective use of these advancing technologies.

OBJECTIVE

This scoping review aims to collate (1) the existing research in Bangladesh's health care system, using the aforementioned technologies and synthesizing their findings, and (2) the limitations faced by researchers in integrating the aforementioned technologies into health care research.

METHODS

MEDLINE (via PubMed), IEEE Xplore, Scopus, and Embase databases were searched to identify published research articles between January 1, 2000, and September 10, 2023, meeting the following inclusion criteria: (1) any study using any of the BDA, AI, ML, and DL technologies and health care and public health datasets for predicting health issues and forecasting any kind of outbreak; (2) studies primarily focusing on health care and public health issues in Bangladesh; and (3) original research articles published in peer-reviewed journals and conference proceedings written in English.

RESULTS

With the initial search, we identified 1653 studies. Following the inclusion and exclusion criteria and full-text review, 4.66% (77/1653) of the articles were finally included in this review. There was a substantial increase in studies over the last 5 years (2017-2023). Among the 77 studies, the majority (n=65, 84%) used ML models. A smaller proportion of studies incorporated AI (4/77, 5%), DL (7/77, 9%), and BDA (1/77, 1%) technologies. Among the reviewed articles, 52% (40/77) relied on primary data, while the remaining 48% (37/77) used secondary data. The primary research areas of focus were infectious diseases (15/77, 19%), noncommunicable diseases (23/77, 30%), child health (11/77, 14%), and mental health (9/77, 12%).

CONCLUSIONS

This scoping review highlights remarkable progress in leveraging BDA, AI, ML, and DL within Bangladesh's health care system. The observed surge in studies over the last 5 years underscores the increasing significance of AI and related technologies in health care research. Notably, most (65/77, 84%) studies focused on ML models, unveiling opportunities for advancements in predictive modeling. This review encapsulates the current state of technological integration and propels us into a promising era for the future of digital Bangladesh.

How can AI-integrated applications affect the financial engineers' psychological safety and work-life balance: Chinese and Iranian financial engineers and administrators' perspectives

BACKGROUND

The integration of AI in finance has significantly reshaped the role of financial engineers, improving efficiency and decision-making. However, it also affects psychological safety and work-life balance. Financial engineers face increased pressure to keep up with evolving technologies, fear of job displacement due to automation, and blurred boundaries between work and personal life. Exploring the link between AI applications, psychological well-being, and work-life balance is crucial for optimizing individual performance and organizational success, ensuring a sustainable and supportive work environment.

OBJECTIVES

This qualitative study investigates how AI-integrated finance applications influence financial engineers' psychological safety and work-life balance. By exploring financial engineers' lived experiences and perceptions, the study seeks to provide insights into the human implications of AI adoption in finance.

METHODOLOGY

The study utilized qualitative research methods, specifically thematic analysis, to examine data from 20 informants selected through theoretical sampling. Thematic analysis techniques were employed to identify recurring patterns, themes, and meanings within the data, allowing for a rich exploration of the research questions.

FINDINGS

Data analysis revealed several themes related to the impact of AI-integrated applications on financial engineers' psychological safety and work-life balance. These themes include the perception of job security, the role of automation in workload management, and the implications of AI for professional identity and job satisfaction.

CONCLUSIONS

This study's findings highlight the multifaceted effects of AI integration in finance, shedding light on the opportunities and challenges it presents for financial engineers. While AI offers potential benefits such as increased efficiency and productivity, it raises concerns about job security and work-related stress. Overall, the study underscores the importance of considering the human implications of AI adoption in finance and calls for proactive measures to support the well-being of financial professionals in an AI-driven environment.

The Perils of Perfection: Navigating the Ripple Effects of Organizational Perfectionism on Employee Misbehavior through Job Insecurity and the Buffering Role of AI Learning Self-Efficacy

This study investigates the complex interplay between organizationally prescribed perfectionism (OPP), job insecurity, counterproductive work behavior (CWB), and self-efficacy in learning artificial intelligence (AI) in the context of modern organizations. Based on several theories, the current research suggests and tests a moderated mediation model. Using a three-wave time-lagged design with data collected from 412 workers across various South Korean corporations, we examine how OPP influences CWB both directly and indirectly through job insecurity, and how self-efficacy in AI learning moderates the OPP-job insecurity link. Our results show that OPP is positively linked to CWB, and this association is partially mediated by job insecurity. Moreover, AI learning self-efficacy functions as a moderator in the OPP-job insecurity link, such that the positive link is weaker for members with higher levels of AI learning self-efficacy. These findings extend our understanding of perfectionism in organizational settings and highlight the role of technological self-efficacy in mitigating the negative impacts of perfectionist cultures. This research may contribute to the literature on perfectionism, CWB, and technological adaptation at work, and has important implications for managing high-performance cultures in the period of rapid technological advancement.

Quantifying the use and potential benefits of artificial intelligence in scientific research

The rapid advancement of artificial intelligence (AI) is poised to reshape almost every line of work. Despite enormous efforts devoted to understanding AI's economic impacts, we lack a systematic understanding of the benefits to scientific research associated with the use of AI. Here we develop a measurement framework to estimate the direct use of AI and associated benefits in science. We find that the use and benefits of AI appear widespread throughout the sciences, growing especially rapidly since 2015. However, there is a substantial gap between AI education and its application in research, highlighting a misalignment between AI expertise supply and demand. Our analysis also reveals demographic disparities, with disciplines with higher proportions of women or Black scientists reaping fewer benefits from AI, potentially exacerbating existing inequalities in science. These findings have implications for the equity and sustainability of the research enterprise, especially as the integration of AI with science continues to deepen.

Improving Social Bot Detection Through Aid and Training

OBJECTIVE

We test the effects of three aids on individuals' ability to detect social bots among Twitter personas: a bot indicator score, a training video, and a warning.

BACKGROUND

Detecting social bots can prevent online deception. We use a simulated social media task to evaluate three aids.

METHOD

Lay participants judged whether each of 60 Twitter personas was a human or social bot in a simulated online environment, using agreement between three machine learning algorithms to estimate the probability of each persona being a bot. Experiment 1 compared a control group and two intervention groups, one provided a bot indicator score for each tweet; the other provided a warning about social bots. Experiment 2 compared a control group and two intervention groups, one receiving the bot indicator scores and the other a training video, focused on heuristics for identifying social bots.

RESULTS

The bot indicator score intervention improved predictive performance and reduced overconfidence in both experiments. The training video was also effective, although somewhat less so. The warning had no effect. Participants rarely reported willingness to share content for a persona that they labeled as a bot, even when they agreed with it.

CONCLUSIONS

Informative interventions improved social bot detection; warning alone did not.

APPLICATION

We offer an experimental testbed and methodology that can be used to evaluate and refine interventions designed to reduce vulnerability to social bots. We show the value of two interventions that could be applied in many settings.

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.

Critical factors challenging the integration of AI technologies in healthcare workplaces: a stakeholder assessment

PURPOSE

This study aims to identify and assess the factors challenging the integration of artificial intelligence (AI) technologies in healthcare workplaces.

DESIGN/METHODOLOGY/APPROACH

The study utilized a mixed approach, that starts with a literature review, then developing and testing a questionnaire survey of the factors challenging the integration of AI technologies in healthcare workplaces. In total, 46 factors were identified and classified under 6 groups. These factors were assessed by four different stakeholder categories: facilities managers, medical staff, operational staff and patients/visitors. The evaluations gathered were examined to determine the relative importance index (RII), importance rating (IR) and ranking of each factor.

FINDINGS

All 46 factors were assessed as "Very Important" through the overall assessment by the four stakeholder categories. The results indicated that the most important factors, across all groups, are "AI ability to learn from patient data", "insufficient data privacy measures for patients", "availability of technical support and maintenance services", "physicians' acceptance of AI in healthcare", "reliability and uptime of AI systems" and "ability to reduce medical errors".

PRACTICAL IMPLICATIONS

Determining the importance ratings of the factors can lead to better resource allocation and the development of strategies to facilitate the adoption and implementation of these technologies, thus promoting the development of innovative solutions to improve healthcare practices.

ORIGINALITY/VALUE

This study contributes to the body of knowledge in the domain of technology adoption and implementation in the medical workplace, through improving stakeholders' comprehension of the factors challenging the integration of AI technologies.

The Usability and Feasibility of a Dietary Intake Self-Monitoring Application in a Population with Varying Literacy Levels

OBJECTIVES

Our aim was to study how hemodialysis patients with varying levels of literacy would use a diet and fluid intake monitoring mobile application (DIMA-P) and what would be its impact on their dietary behaviors.

MATERIALS AND METHODS

We developed a mobile application using user-centered methods and informed by the Integrated Theory of Health Behavior Change (ITHBC). Eight hemodialysis patients were recruited to use the application to record and monitor their diet and fluid intakes for a 6-week study. Overall, the participants had low literacy, numeracy, and technical skills. We collected the data on application usage and administered usability and context-of-use questionnaires to gain insights into the participants' interaction with the application. The participants' portion estimation skills and dietary self-regulation self-efficacy were assessed using various tests. In addition, interdialytic weight gain data were collected to assess the impact of app usage on the participants' health outcomes.

RESULTS

The application usage patterns varied, with a general trend towards frequent use (n = 5) correlating with engagement in self-monitoring. The participants gave high comprehensibility, user-friendliness, satisfaction, and usefulness ratings, suggesting that the app was well designed and the target users could easily navigate and interact with the features. While the participants improved in estimating portion sizes, the impact on measuring skills was variable. There was also an improvement in the participants' dietary self-regulation self-efficacy post-study. The interdialytic weight gain trends indicated a slight improvement in fluid and diet management.

CONCLUSION

People with different literacy skills can effectively use icon-based interfaces for portion size estimation and develop personalized usage patterns to self-regulate their fluid and dietary intakes. Moreover, they can experience an enhancement in their dietary self-efficacy skills by using a mobile application aimed at providing nutritional feedback. Furthermore, this research shows that the constructs of the ITHBC are effective in promoting dietary behavior change in a population with varying literacy skills. The target users can benefit from explicitly visualizing the relationship between their health outcomes and the factors influencing those outcomes. These user ambitions could be supported by developing machine learning models. Future research should also focus on enhancing the mechanisms by which technology can further enhance each component of the ITHBC framework.

Trends in publication impact of evidence-based healthcare terminology (2013-2022)

AIMS

This article explored the publication impact of evidence-based healthcare terminology to determine usage and discuss options for low usage terms.

BACKGROUND

A plethora of terms describe the scholarship of evidence-based healthcare. Several terms are synonyms, creating redundancy and confusion. The abundance and overlap of terms may impede the discovery of evidence.

DESIGN

This discursive article explored and discussed publication impact of evidence-based healthcare terms.

METHODS

Evidence-based healthcare terms were identified, and their 10-year (2013-2022) publication impact was assessed in the CINAHL and Medline databases. A card sort method was also used to identify terms with low usage.

RESULTS

A total of 18/32 terms were included in the review. The terms evidence-based practice, quality improvement, research and translational research were the most highly published terms. Publication data were presented yearly over a 10-year period. Most terms increased in publication use over time, except for three terms whose use decreased. Several terms related to translational research have multiple synonyms. It remains unknown whether these terms are interchangeable and possibly redundant, or if there are nuanced differences between terms.

CONCLUSION

We suggest a follow-up review in 3-5 years to identify publication trends to assess context and terms with continued low publication usage. Terms with persistent low usage should be considered for retirement in the reporting of scholarly activities. Additionally, terms with increasing publication trends should be treated as emerging terms that contribute to evidence-based healthcare terminology.

IMPLICATIONS FOR NURSING

Confusion about the use of appropriate terminology may hinder progress in the scholarship of evidence-based healthcare. We encourage scholars to be aware of publication impact as it relates to the use of specific terminology and be purposeful in the selection of terms used in scholarly projects and publications.

Bibliometric analysis of artificial intelligence in healthcare research: Trends and future directions

Objective

The presence of artificial intelligence (AI) in healthcare is a powerful and game-changing force that is completely transforming the industry as a whole. Using sophisticated algorithms and data analytics, AI has unparalleled prospects for improving patient care, streamlining operational efficiency, and fostering innovation across the healthcare ecosystem. This study conducts a comprehensive bibliometric analysis of research on AI in healthcare, utilising the SCOPUS database as the primary data source.

Methods

Preliminary findings from 2013 identified 153 publications on AI and healthcare. Between 2019 and 2023, the number of publications increased exponentially, indicating significant growth and development in the field. The analysis employs various bibliometric indicators to assess research production performance, science mapping techniques, and thematic mapping analysis.

Results

The study reveals insights into research hotspots, thematic focus, and emerging trends in AI and healthcare research. Based on an extensive examination of the Scopus database provides a brief overview and suggests potential avenues for further investigation.

Conclusion

This article provides valuable contributions to understanding the current landscape of AI in healthcare, offering insights for future research directions and informing strategic decision making in the field.

The state of artificial intelligence in medical research: A survey of corresponding authors from top medical journals

Natural Language Processing (NLP) is a subset of artificial intelligence that enables machines to understand and respond to human language through Large Language Models (LLMs)‥ These models have diverse applications in fields such as medical research, scientific writing, and publishing, but concerns such as hallucination, ethical issues, bias, and cybersecurity need to be addressed. To understand the scientific community's understanding and perspective on the role of Artificial Intelligence (AI) in research and authorship, a survey was designed for corresponding authors in top medical journals. An online survey was conducted from July 13th, 2023, to September 1st, 2023, using the SurveyMonkey web instrument, and the population of interest were corresponding authors who published in 2022 in the 15 highest-impact medical journals, as ranked by the Journal Citation Report. The survey link has been sent to all the identified corresponding authors by mail. A total of 266 authors answered, and 236 entered the final analysis. Most of the researchers (40.6%) reported having moderate familiarity with artificial intelligence, while a minority (4.4%) had no associated knowledge. Furthermore, the vast majority (79.0%) believe that artificial intelligence will play a major role in the future of research. Of note, no correlation between academic metrics and artificial intelligence knowledge or confidence was found. The results indicate that although researchers have varying degrees of familiarity with artificial intelligence, its use in scientific research is still in its early phases. Despite lacking formal AI training, many scholars publishing in high-impact journals have started integrating such technologies into their projects, including rephrasing, translation, and proofreading tasks. Efforts should focus on providing training for their effective use, establishing guidelines by journal editors, and creating software applications that bundle multiple integrated tools into a single platform.

Online communities come with real-world consequences for individuals and societies

Online communities have become a central part of the internet. Understanding what motivates users to join these communities, and how they affect them and others, spans various psychological domains, including organizational psychology, political and social psychology, and clinical and health psychology. We focus on online communities that are exemplary for three domains: work, hate, and addictions. We review the risks that emerge from these online communities but also recognize the opportunities that work and behavioral addiction communities present for groups and individuals. With the continued evolution of online spheres, online communities are likely to have an increasingly significant role in all spheres of life, ranging from personal to professional and from individual to societal. Psychological research provides critical insights into understanding the formation of online communities, and the implications for individuals and society. To counteract risks, it needs to identify opportunities for prevention and support.

Artificial intelligence probabilities scheme for disease prevention data set construction in intelligent smart healthcare scenario

In the face of an aging population, smart healthcare services are now within reach, thanks to the proliferation of high-speed internet and other forms of digital technology. Data problems in smart healthcare, unfortunately, put artificial intelligence in this area to serious limitations. There are several issues, including a lack of standard samples, noisy data interference, and actual data that is missing. A three-stage AI-based data generating strategy is suggested to handle missing datasets, using a small sample dataset obtained from a smart healthcare program community in a specific city: Step one involves generating the dataset's basic attributes using a tree-based generation strategy that takes the original data distribution into account. Step two involves using the Naive Bayes algorithm to create basic indicators of behavioural capability assessment for the samples. Step three builds on stage two and uses a multivariate linear regression method to create evaluation criteria and indicators of high-level behavioural capability. Six problems involving multiple classifications and two tasks using multiple labels are implemented using various neural network-based training strategies on the obtained data to assess the usefulness of the dataset for downstream tasks. To ensure that the data collected is genuine and useful, the experimental data must be analysed and expert knowledge must be included.

Perceptions and attitudes of health science students relating to artificial intelligence (AI): A scoping review

Background and Aims

The recent integration of artificial intelligence (AI) across education, research, and clinical healthcare has led to a growing interest in AI training for healthcare students. This scoping review seeks to delve into existing literature, aiming to evaluate the perceptions and attitudes, of health science students toward the implementation of AI in their field.

Methods

This review followed the methodological guidance offered by Arksey and O'Malley and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR). A systematic search was conducted in the databases Medline, Emcare, and Scopus. Studies using both quantitative and qualitative methodologies were eligible if they explored the perceptions or attitudes of health science students in relation to AI. Relevant data from eligible articles was extracted and analyzed using narrative synthesis.

Results

Ten studies were included. Articles reported on the primary outcomes of perceptions (i.e., thoughts, ideas, satisfaction, etc.) and attitudes (i.e., beliefs, tendencies, etc.). Disciplines included nursing, diagnostic radiography, pharmacy, midwifery, occupational therapy, physiotherapy, and speech pathology were featured. Overall, students felt positively about the potential benefits AI would have on their future work. Students' interest and willingness to learn about AI was also favorable. Studies evaluating attitudes found positive correlations between attitudes toward AI, AI utilization, and intention to use AI. Negative perceptions related to threats of job security, and a lack of realism associated with AI software.

Conclusion

Overall, evidence from this review indicates that health science students' worldwide hold positive perceptions toward AI. Educators should focus on instilling positive attitudes toward AI, given correlations between AI exposure and intention to adopt AI.

Optimizing wastewater treatment through artificial intelligence: recent advances and future prospects

Artificial intelligence (AI) is increasingly being applied to wastewater treatment to enhance efficiency, improve processes, and optimize resource utilization. This review focuses on objectives, advantages, outputs, and major findings of various AI models in the three key aspects: the prediction of removal efficiency for both organic and inorganic pollutants, real-time monitoring of essential water quality parameters (such as pH, COD, BOD, turbidity, TDS, and conductivity), and fault detection in the processes and equipment integral to wastewater treatment. The prediction accuracy (R2 value) of AI technologies for pollutant removal has been reported to vary between 0.64 and 1.00. A critical aspect explored in this review is the cost-effectiveness of implementing AI systems in wastewater treatment. Numerous countries and municipalities are actively engaging in pilot projects and demonstrations to assess the feasibility and effectiveness of AI applications in wastewater treatment. Notably, the review highlights successful outcomes from these initiatives across diverse geographical contexts, showcasing the adaptability and positive impact of AI in revolutionizing wastewater treatment on a global scale. Further, insights on the ethical considerations and potential future directions for the use of AI in wastewater treatment plants have also been provided.

Artificial Intelligence Readiness, Perceptions, and Educational Needs Among Dental Students: A Cross-Sectional Study

OBJECTIVES

With Artificial Intelligence (AI) profoundly affecting education, ensuring that students in health disciplines are ready to embrace AI is essential for their future workforce integration. This study aims to explore dental students' readiness to use AI, perceptions about AI in health education and healthcare, and their AI-related educational needs.

MATERIAL AND METHODS

A cross-sectional survey was conducted among dental students at the College of Dental Medicine, Qatar University. The survey assessed readiness for AI using the Medical Artificial Intelligence Readiness Scale (MAIRS). Students' perceptions of AI in healthcare and health education and their educational needs were also explored.

RESULTS

A total of 94 students responded to the survey. AI readiness scores were average (3.3 ± 0.64 out of 5); while participants appeared more ready for the vision and ethics domains of MAIRS, they showed less readiness regarding cognition and ability. Participants scored average on AI perceptions (3.35 ± 0.45 out of 5), with concerns regarding AI risks and disadvantages. They expressed a high need for knowledge and skills related to AI use in healthcare (84%), AI for health-related research (81.9%), and AI in radiology and imaging procedures (79.8%). Student readiness had a significant correlation with AI perceptions and perceived level of AI knowledge.

CONCLUSIONS

This is the first study in Qatar exploring dental students' AI readiness, perceptions, and educational needs regarding AI applications in education and healthcare. The perceived AI knowledge gaps could inform future curricular AI integration. Advancing AI skills and deepening AI comprehension can empower future dental professionals through anticipated advances in the AI-driven healthcare landscape.

Comparison of the Efficacy of Artificial Intelligence-Powered Software in Crown Design: An In Vitro Study

INTRODUCTION AND AIMS

Artificial intelligence (AI) has been adopted in the field of dental restoration. This study aimed to evaluate the time efficiency and morphological accuracy of crowns designed by two AI-powered software programs in comparison with conventional computer-aided design software.

METHODS

A total of 33 clinically adapted posterior crowns were involved in the standard group. AI Automate (AA) and AI Dentbird Crown (AD) used two AI-powered design software programs, while the computer-aided experienced and computer-aided novice employed the Exocad DentalCAD software. Time efficiency between the AI-powered groups and computer-aided groups was evaluated by assessing the elapsed time. Morphological accuracy was assessed by means of three-dimensional geometric calculations, with the root-mean-square error compared against the standard group. Statistical analysis was conducted via the Kruskal-Wallis test (α = 0.05).

RESULTS

The time efficiency of the AI-powered groups was significantly higher than that of the computer-aided groups (P < .01). Moreover, the working time for both AA and AD groups was only one-quarter of that for the computer-aided novice group. Four groups significantly differed in morphological accuracy for occlusal and distal surfaces (P < .05). The AD group performed lower accuracy than the other three groups on the occlusal surfaces (P < .001) and the computer-aided experienced group was superior to the AA group in terms of accuracy on the distal surfaces (P = .029). However, morphological accuracy showed no significant difference among the four groups for mesial surfaces and margin lines (P > .05).

CONCLUSION

AI-powered software enhanced the efficiency of crown design but failed to excel at morphological accuracy compared with experienced technicians using computer-aided software. AI-powered software requires further research and extensive deep learning to improve the morphological accuracy and stability of the crown design.

Errors in Radiology: A Standard Review

Radiological interpretations, while essential, are not infallible and are best understood as expert opinions formed through the evaluation of available evidence. Acknowledging the inherent possibility of error is crucial, as it frames the discussion on improving diagnostic accuracy and patient care. A comprehensive review of error classifications highlights the complexity of diagnostic errors, drawing on recent frameworks to categorize them into perceptual and cognitive errors, among others. This classification underpins an analysis of specific error types, their prevalence, and implications for clinical practice. Additionally, we address the psychological impact of radiological practice, including the effects of mental health and burnout on diagnostic accuracy. The potential of artificial intelligence (AI) in mitigating errors is discussed, alongside ethical and regulatory considerations in its application. This research contributes to the body of knowledge on radiological errors, offering insights into preventive strategies and the integration of AI to enhance diagnostic practices. It underscores the importance of a nuanced understanding of errors in radiology, aiming to foster improvements in patient care and radiological accuracy.

Paediatric nurses' perspectives on artificial intelligence applications: A cross-sectional study of concerns, literacy levels and attitudes

AIMS

This study aimed to explore the correlation between artificial intelligence (AI) literacy, AI anxiety and AI attitudes among paediatric nurses, as well as identify the influencing factors on paediatric nurses' AI attitudes.

DESIGN

A descriptive, correlational and cross-sectional research.

METHODS

This study was conducted between January and February 2024 with 170 nurses actively working in paediatric clinics in Turkey. The data collection tools included the Nurse Information Form, the General Attitudes Towards Artificial Intelligence Scale (GAAIS), the Artificial Intelligence Literacy Scale (AILS) and the Artificial Intelligence Anxiety Scale (AIAS). To determine the associations between the variables, the data was analysed using IBM SPSS 28, which included linear regression and Pearson correlation analysis.

RESULTS

The study indicated significant positive correlations between paediatric nurses' age and their AIAS scores (r = .226; p < .01) and significant negative correlations between paediatric nurses' age and their AILS (r = -.192; p < .05) and GAAIS scores (r = -.152; p < .05). The GAAIS was significantly predictive (p < .000) and accounted for 50% of the variation in AIAS and AILS scores.

CONCLUSION

Paediatric nurses' attitudes towards AI significantly predicted AI literacy and AI anxiety. The relationship between the age of the paediatric nurses and the anxiety, AI literacy and attitudes towards AI was demonstrated. Healthcare and educational institutions should create customized training programs and awareness-raising activities for older nurses, as there are noticeable variations in the attitudes of paediatric nurses towards AI based on their age.

IMPLICATIONS FOR PROFESSION AND/OR PATIENT CARE

Providing in-service AI training can help healthcare organizations improve paediatric nurses' attitudes towards AI, increase their AI literacy and reduce their anxiety. This training has the potential to impact their attitudes positively and reduce their anxiety.

REPORTING METHOD

The study results were critically reported using STROBE criteria.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

The impact of AI-based decision support systems on nursing workflows in critical care units

AIM

This research examines the effects of artificial intelligence (AI)-based decision support systems (DSS) on the operational processes of nurses in critical care units (CCU) located in Amman, Jordan.

BACKGROUND

The deployment of AI technology within the healthcare sector presents substantial opportunities for transforming patient care, with a particular emphasis on the field of nursing.

METHOD

This paper examines how AI-based DSS affect CCU nursing workflows in Amman, Jordan, using a cross-sectional analysis. A study group of 112 registered nurses was enlisted throughout a research period spanning one month. Data were gathered using surveys that specifically examined several facets of nursing workflows, the employment of AI, encountered problems, and the sufficiency of training.

RESULT

The findings indicate a varied demographic composition among the participants, with notable instances of AI technology adoption being reported. Nurses have the perception that there are favorable effects on time management, patient monitoring, and clinical decision-making. However, they continue to face persistent hurdles, including insufficient training, concerns regarding data privacy, and technical difficulties.

DISCUSSION

The study highlights the significance of thorough training programs and supportive mechanisms to improve nurses' involvement with AI technologies and maximize their use in critical care environments. Although there are differing degrees of contentment with existing AI systems, there is a general agreement on the necessity of ongoing enhancement and fine-tuning to optimize their efficacy in enhancing patient care results.

CONCLUSION AND IMPLICATIONS FOR NURSING AND/OR HEALTH POLICY

This research provides essential knowledge about the intricacies of incorporating AI into nursing practice, highlighting the significance of tackling obstacles to guarantee the ethical and efficient use of AI technology in healthcare.

Who are the publics engaging in AI?

Given the importance of public engagement in governments' adoption of artificial intelligence systems, artificial intelligence researchers and practitioners spend little time reflecting on who those publics are. Classifying publics affects assumptions and affordances attributed to the publics' ability to contribute to policy or knowledge production. Further complicating definitions are the publics' role in artificial intelligence production and optimization. Our structured analysis of the corpus used a mixed method, where algorithmic generation of search terms allowed us to examine approximately 2500 articles and provided the foundation to conduct an extensive systematic literature review of approximately 100 documents. Results show the multiplicity of ways publics are framed, by examining and revealing the different semantic nuances, affordances, political and expertise lenses, and, finally, a lack of definitions. We conclude that categorizing publics represents an act of power, politics, and truth-seeking in artificial intelligence.

Evaluating the readiness of healthcare administration students to utilize AI for sustainable leadership: a survey study

PURPOSE

This paper explores how healthcare administration students perceive the integration of Artificial Intelligence (AI) in healthcare leadership, mainly focusing on the sustainability aspects involved. It aims to identify gaps in current educational curricula and suggests enhancements to better prepare future healthcare professionals for the evolving demands of AI-driven healthcare environments.

DESIGN/METHODOLOGY/APPROACH

This study utilized a cross-sectional survey design to understand healthcare administration students' perceptions regarding integrating AI in healthcare leadership. An online questionnaire, developed from an extensive literature review covering fundamental AI knowledge and its role in sustainable leadership, was distributed to students majoring and minoring in healthcare administration. This methodological approach garnered participation from 62 students, providing insights and perspectives crucial for the study's objectives.

FINDINGS

The research revealed that while a significant majority of healthcare administration students (70%) recognize the potential of AI in fostering sustainable leadership in healthcare, only 30% feel adequately prepared to work in AI-integrated environments. Additionally, students were interested in learning more about AI applications in healthcare and the role of AI in sustainable leadership, underscoring the need for comprehensive AI-focused education in their curriculum.

RESEARCH LIMITATIONS/IMPLICATIONS

The research is limited by its focus on a single academic institution, which may not fully represent the diversity of perspectives in healthcare administration.

PRACTICAL IMPLICATIONS

This study highlights the need for healthcare administration curricula to incorporate AI education, aligning theoretical knowledge with practical applications, to effectively prepare future professionals for the evolving demands of AI-integrated healthcare environments.

ORIGINALITY/VALUE

This research paper presents insights into healthcare administration students' readiness and perspectives toward AI integration in healthcare leadership, filling a critical gap in understanding the educational needs in the evolving landscape of AI-driven healthcare.

Assessment of Saudi Public Perceptions and Opinions towards Artificial Intelligence in Health Care

Background and Objectives: The healthcare system in Saudi Arabia is growing rapidly with the utilization of advanced technologies. Therefore, this study aimed to assess the Saudi public perceptions and opinions towards artificial intelligence (AI) in health care. Materials and Methods: This cross-sectional web-based questionnaire study was conducted between January and April 2024. Data were analyzed from 830 participants. The perceptions of the public towards AI were assessed using 21-item questionnaires. Results: Among the respondents, 69.4% were males and 46% of them were aged above 41 years old. A total of 84.1% of the participants knew about AI, while 61.1% of them believed that AI is a tool that helps healthcare professionals, and 12.5% of them thought that AI may replace the physician, pharmacist, or nurse in the healthcare system. With regard to opinion on the widespread use of AI, 45.8% of the study population believed that healthcare professionals will be improved with the widespread use of artificial intelligence. The mean perception score of AI among males was 38.4 (SD = 6.1) and this was found to be higher than for females at 37.7 (SD = 5.3); however, no significant difference was observed (p = 0.072). Similarly, the mean perception score was higher among young adults aged between 20 and 25 years at 38.9 (SD = 6.1) compared to other age groups, but indicating no significant association between them (p = 0.198). Conclusions: The results showed that the Saudi public had a favorable opinion and perceptions of AI in health care. This suggests that health management recommendations should be made regarding how to successfully integrate and use medical AI while maintaining patient safety.

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.

A Theoretical Exploration of Artificial Intelligence's Impact on Feto-Maternal Health from Conception to Delivery

The implementation of Artificial Intelligence (AI) in healthcare is enhancing diagnostic accuracy in clinical setups. The use of AI in healthcare is steadily increasing with advancing technology, extending beyond disease diagnosis to encompass roles in feto-maternal health. AI harnesses Machine Learning (ML), Natural Language Processing (NLP), Artificial Neural Networks (ANN), and computer vision to analyze data and draw conclusions. Considering maternal health, ML analyzes vast datasets to predict maternal and fetal health outcomes, while NLP interprets medical texts and patient records to assist in diagnosis and treatment decisions. ANN models identify patterns in complex feto-maternal medical data, aiding in risk assessment and intervention planning whereas, computer vision enables the analysis of medical images for early detection of feto-maternal complications. AI facilitates early pregnancy detection, genetic screening, and continuous monitoring of maternal health parameters, providing real-time alerts for deviations, while also playing a crucial role in the early detection of fetal abnormalities through enhanced ultrasound imaging, contributing to informed decision-making. This review investigates into the application of AI, particularly through predictive models, in addressing the monitoring of feto-maternal health. Additionally, it examines potential future directions and challenges associated with these applications.

Organoid bioinks: construction and application

Organoids have emerged as crucial platforms in tissue engineering and regenerative medicine but confront challenges in faithfully mimicking native tissue structures and functions. Bioprinting technologies offer a significant advancement, especially when combined with organoid bioinks-engineered formulations designed to encapsulate both the architectural and functional elements of specific tissues. This review provides a rigorous, focused examination of the evolution and impact of organoid bioprinting. It emphasizes the role of organoid bioinks that integrate key cellular components and microenvironmental cues to more accurately replicate native tissue complexity. Furthermore, this review anticipates a transformative landscape invigorated by the integration of artificial intelligence with bioprinting techniques. Such fusion promises to refine organoid bioink formulations and optimize bioprinting parameters, thus catalyzing unprecedented advancements in regenerative medicine. In summary, this review accentuates the pivotal role and transformative potential of organoid bioinks and bioprinting in advancing regenerative therapies, deepening our understanding of organ development, and clarifying disease mechanisms.

Innovative governance for transformative energy policy in sub-Saharan Africa after COVID-19: Green pathways in Egypt, Nigeria, and South Africa

Learning from innovations that fail is imperative for innovations that succeed. The theoretical underpinnings for this innovative framing are reflexivity, transformative unlearning, and intelligent failure. This framework proposes a definition of "transformative governance" as governance that creates structural equities. Governments rebuilding their economies after the COVID-19 pandemic seek equitable green transformations; that are gendered, structural, and sustainable, learning from the implemented gender-sensitive responses (hereafter referred to as policy innovations). This paper argues that transformative practices, beliefs, values, assumptions, policies, and systematic learnings are complementary to post-crisis transformations. The aim is to promote systematic learnings from innovation governance failure regarding energy policy through the analysis of COVID-19 practices and the unlearning of policy innovation beliefs, values, and assumptions that are not transformative. I ask: how gender-equitable, structurally equitable, and green-transformative were the COVID-19 policy innovations? The study's approach is qualitative and situated within the constructivist research paradigm. It uses reflexive thematic analysis combined with innovative coded policy narrative and a transformative index-matching technique, to identify the gap within transformative interventions. The study included 58 policy innovations (n = 58) collected from the UNDP, KPMG, government reports, and news flashes from the three most populous nations in sub-Sahara Africa: Egypt, Nigeria, and South Africa. The study found that policy innovations were inequitable in terms of gender, structure, and sustainability whereas the derived transformative pathways are equitable and gender-transformative, structurally transformative, and green-transformative. The rationales behind a transformative approach to policy reflect the systemic failures across key areas: market dynamics, research and development, and green transformation. Policy innovators can align transformative pathways for innovative governance that implements transformative energy policy. To address the needs of multiple fragile and vulnerable identities, the derived post-pandemic framework is an intersectional plan with 10 policy learning pillars. The plan includes local energy transformation and reinforcement of energy justice components, such as the localization of the energy industry, community power, and social norms, including Ubuntu, which translates to "I am because we are." Reengagement in global supply chains requires South-South trade relations to be restrategized.

Using Artificial Intelligence to Label Free-Text Operative and Ultrasound Reports for Grading Pediatric Appendicitis

PURPOSE

Data science approaches personalizing pediatric appendicitis management are hampered by small datasets and unstructured electronic medical records (EMR). Artificial intelligence (AI) chatbots based on large language models can structure free-text EMR data. We compare data extraction quality between ChatGPT-4 and human data collectors.

METHODS

To train AI models to grade pediatric appendicitis preoperatively, several data collectors extracted detailed preoperative and operative data from 2100 children operated for acute appendicitis. Collectors were trained for the task based on satisfactory Kappa scores. ChatGPT-4 was prompted to structure free text from 103 random anonymized ultrasound and operative records in the dataset using the set variables and coding options, and to estimate appendicitis severity grade from the operative report. A pediatric surgeon then adjudicated all data, identifying errors in each method.

RESULTS

Within the 44 ultrasound (42.7%) and 32 operative reports (31.1%) discordant in at least one field, 98% of the errors were found in the manual data extraction. The appendicitis grade was erroneously assigned manually in 29 patients (28.2%), and by ChatGPT-4 in 3 (2.9%). Across datasets, the use of the AI chatbot was able to avoid misclassification in 59.2% of the records including both reports and extracted data approximately 40 times faster.

CONCLUSION

AI chatbot significantly outperformed manual data extraction in accuracy for ultrasound and operative reports, and correctly assigned the appendicitis grade. While wider validation is required and data safety concerns must be addressed, these AI tools show significant promise in improving the accuracy and efficiency of research data collection.

LEVELS OF EVIDENCE

Level III.

AI can empower agriculture for global food security: challenges and prospects in developing nations

Food and nutrition are a steadfast essential to all living organisms. With specific reference to humans, the sufficient and efficient supply of food is a challenge as the world population continues to grow. Artificial Intelligence (AI) could be identified as a plausible technology in this 5th industrial revolution in bringing us closer to achieving zero hunger by 2030-Goal 2 of the United Nations Sustainable Development Goals (UNSDG). This goal cannot be achieved unless the digital divide among developed and underdeveloped countries is addressed. Nevertheless, developing and underdeveloped regions fall behind in economic resources; however, they harbor untapped potential to effectively address the impending demands posed by the soaring world population. Therefore, this study explores the in-depth potential of AI in the agriculture sector for developing and under-developed countries. Similarly, it aims to emphasize the proven efficiency and spin-off applications of AI in the advancement of agriculture. Currently, AI is being utilized in various spheres of agriculture, including but not limited to crop surveillance, irrigation management, disease identification, fertilization practices, task automation, image manipulation, data processing, yield forecasting, supply chain optimization, implementation of decision support system (DSS), weed control, and the enhancement of resource utilization. Whereas AI supports food safety and security by ensuring higher crop yields that are acquired by harnessing the potential of multi-temporal remote sensing (RS) techniques to accurately discern diverse crop phenotypes, monitor land cover dynamics, assess variations in soil organic matter, predict soil moisture levels, conduct plant biomass modeling, and enable comprehensive crop monitoring. The present study identifies various challenges, including financial, infrastructure, experts, data availability, customization, regulatory framework, cultural norms and attitudes, access to market, and interdisciplinary collaboration, in the adoption of AI for developing nations with their subsequent remedies. The identification of challenges and opportunities in the implementation of AI could ignite further research and actions in these regions; thereby supporting sustainable development.

Unpacking public resistance to health Chatbots: a parallel mediation analysis

Introduction

Despite the numerous potential benefits of health chatbots for personal health management, a substantial proportion of people oppose the use of such software applications. Building on the innovation resistance theory (IRT) and the prototype willingness model (PWM), this study investigated the functional barriers, psychological barriers, and negative prototype perception antecedents of individuals' resistance to health chatbots, as well as the rational and irrational psychological mechanisms underlying their linkages.

Methods

Data from 398 participants were used to construct a partial least squares structural equation model (PLS-SEM).

Results

Resistance intention mediated the relationship between functional barriers, psychological barriers, and resistance behavioral tendency, respectively. Furthermore, The relationship between negative prototype perceptions and resistance behavioral tendency was mediated by resistance intention and resistance willingness. Moreover, negative prototype perceptions were a more effective predictor of resistance behavioral tendency through resistance willingness than functional and psychological barriers.

Discussion

By investigating the role of irrational factors in health chatbot resistance, this study expands the scope of the IRT to explain the psychological mechanisms underlying individuals' resistance to health chatbots. Interventions to address people's resistance to health chatbots are discussed.

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.

AI energized hydrogel design, optimization and application in biomedicine

Traditional hydrogel design and optimization methods usually rely on repeated experiments, which is time-consuming and expensive, resulting in a slow-moving of advanced hydrogel development. With the rapid development of artificial intelligence (AI) technology and increasing material data, AI-energized design and optimization of hydrogels for biomedical applications has emerged as a revolutionary breakthrough in materials science. This review begins by outlining the history of AI and the potential advantages of using AI in the design and optimization of hydrogels, such as prediction and optimization of properties, multi-attribute optimization, high-throughput screening, automated material discovery, optimizing experimental design, and etc. Then, we focus on the various applications of hydrogels supported by AI technology in biomedicine, including drug delivery, bio-inks for advanced manufacturing, tissue repair, and biosensors, so as to provide a clear and comprehensive understanding of researchers in this field. Finally, we discuss the future directions and prospects, and provide a new perspective for the research and development of novel hydrogel materials for biomedical applications.

Multidisciplinary collaboration on exoskeleton development adopting user-centered design: a systematic integrative review

Purpose: The world population is ageing, along with an increasing possibility of functional limitations that affect independent living. Assistive technologies such as exoskeletons for rehabilitative purposes and daily activities assistance maintaining the independence of people with disabilities, especially older adults who wish to ageing-in-place. The purpose of this systematic integrative review was threefold: to explore the development team compositions and involvement, to understand the recruitment and engagement of stakeholders, and to synthesise reported or anticipated consequences of multidisciplinary collaboration.Methods: Databases searched included PubMed, CINAHL Plus, PsycINFO, Web of Science, Scopus, and IEEE Xplore. A total of 34 studies that reported the development of exoskeleton adopting user-centered design (UCD) method in healthcare or community settings that were published in English from 2000 to July 2022 were included.Results: Three major trends emerged from the analysis of included studies. First, there is a need to redefine multidisciplinary collaboration, from within-discipline collaboration to cross-discipline collaboration. Second, the level of engagement of stakeholders during the exoskeleton development remained low. Third, there was no standardised measurement to quantify knowledge production currently.Conclusion: As suggested by the synthesised results in this review, exoskeleton development has been increasing to improve the functioning of people with disabilities. Exoskeleton development often required expertise from different disciplines and the involvement of stakeholders to increase acceptance, thus we propose the Multidisciplinary Collaboration Appraisal Tool to assess multidisciplinary collaboration using the UCD approach. Future research is required to understand the effectiveness of multidisciplinary collaboration on exoskeleton development using the UCD approach.IMPLICATIONS FOR REHABILITATIONGlobal trend of population ageing causes a higher risk of disability in older adults who require rehabilitation and assistance in daily living.Assistive technologies such as exoskeletons have the potential to contribute to rehabilitation training and daily activity assistance demand closer multidisciplinary collaboration.A Multidisciplinary Collaboration Appraisal Tool using user-centered design approach (MCAT) is proposed to understand the effectiveness as well as limitations and barriers associated with multidisciplinary collaboration in developing exoskeletons.

The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century

As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI's transformative potential, this review equips researchers with a deeper understanding of AI's current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.

A Comprehensive Evaluation of AI-Assisted Diagnostic Tools in ENT Medicine: Insights and Perspectives from Healthcare Professionals

The integration of Artificial Intelligence (AI) into healthcare has the potential to revolutionize medical diagnostics, particularly in specialized fields such as Ear, Nose, and Throat (ENT) medicine. However, the successful adoption of AI-assisted diagnostic tools in ENT practice depends on the understanding of various factors; these include influences on their effectiveness and acceptance among healthcare professionals. This cross-sectional study aimed to assess the usability and integration of AI tools in ENT practice, determine the clinical impact and accuracy of AI-assisted diagnostics in ENT, measure the trust and confidence of ENT professionals in AI tools, gauge the overall satisfaction and outlook on the future of AI in ENT diagnostics, and identify challenges, limitations, and areas for improvement in AI-assisted ENT diagnostics. A structured online questionnaire was distributed to 600 certified ENT professionals with at least one year of experience in the field. The questionnaire assessed participants' familiarity with AI tools, usability, clinical impact, trust, satisfaction, and identified challenges. A total of 458 respondents completed the questionnaire, resulting in a response rate of 91.7%. The majority of respondents reported familiarity with AI tools (60.7%) and perceived them as generally usable and clinically impactful. However, challenges such as integration with existing systems, user-friendliness, accuracy, and cost were identified. Trust and satisfaction levels varied among participants, with concerns regarding data privacy and support. Geographic and practice setting differences influenced perceptions and experiences. The study highlights the diverse perceptions and experiences of ENT professionals regarding AI-assisted diagnostics. While there is general enthusiasm for these tools, challenges related to integration, usability, trust, and cost need to be addressed for their widespread adoption. These findings provide valuable insights for developers, policymakers, and healthcare providers aiming to enhance the role of AI in ENT practice.

Artificial intelligence in future nursing care: Exploring perspectives of nursing professionals - A descriptive qualitative study

Background

The healthcare landscape is rapidly evolving, with artificial intelligence (AI) emerging as a transformative force. In this context, understanding the viewpoints of nursing professionals regarding the integration of AI in future nursing care is crucial.

Aims

This study aimed to provide insights into the perceptions of nursing professionals regarding the role of AI in shaping the future of healthcare.

Methods

A cohort of 23 nursing professionals was recruited between April 7, 2023, and May 4, 2023, for this study. Employing a thematic analysis approach, qualitative data from interviews with nursing professionals were analyzed. Verbatim transcripts underwent rigorous coding, and these codes were organized into themes through constant comparative analysis. The themes were refined and developed through the grouping of related codes, ensuring an authentic representation of participants' viewpoints.

Results

After careful data analysis, ten key themes emerged including: (I) Perceptions of AI readiness; (II) Benefits and concerns; (III) Enhanced patient outcomes; (IV) Collaboration and workflow; (V) Human-tech balance: (VI) Training and skill development; (VII) Ethical and legal considerations; (VIII) AI implementation barriers; (IX) Patient-nurse relationships; (X) Future vision and adaptation.

Conclusion

This study provides valuable insights into nursing professionals' perspectives on the integration of AI in future nursing care. It highlights their enthusiasm for AI's potential benefits while emphasizing the importance of ethical and compassionate nursing practice. The findings underscore the need for comprehensive training programs to equip nursing professionals with the skills necessary for successful AI integration. Ultimately, this research contributes to the ongoing discourse on the role of AI in nursing, paving the way for a future where innovative technologies complement and enhance the delivery of patient-centered care.

IUPHAR Review: New strategies for medications to treat substance use disorders

Substance use disorders (SUDs) and drug overdose are a public health emergency and safe and effective treatments are urgently needed. Developing new medications to treat them is expensive, time-consuming, and the probability of a compound progressing to clinical trials and obtaining FDA-approval is low. The small number of FDA-approved medications for SUDs reflects the low interest of pharmaceutical companies to invest in this area due to market forces, characteristics of the population (e.g., stigma, and socio-economic and legal disadvantages), and the high bar regulatory agencies set for new medication approval. In consequence, most research on medications is funded by government agencies, such as the National Institute on Drug Abuse (NIDA). Multiple scientific opportunities are emerging that can accelerate the discovery and development of new medications for SUDs. These include fast and efficient tools to screen new molecules, discover new medication targets, use of big data to explore large clinical data sets and artificial intelligence (AI) applications to make predictions, and precision medicine tools to individualize and optimize treatments. This review provides a general description of these new research strategies for the development of medications to treat SUDs with emphasis on the gaps and scientific opportunities. It includes a brief overview of the rising public health toll of SUDs; the justification, challenges, and opportunities to develop new medications; and a discussion of medications and treatment endpoints that are being evaluated with support from NIDA.

Tooth morphology, internal fit, occlusion and proximal contacts of dental crowns designed by deep learning-based dental software: A comparative study

OBJECTIVES

This study compared the tooth morphology, internal fit, occlusion, and proximal contacts of dental crowns automatically generated via two deep learning (DL)-based dental software systems with those manually designed by an experienced dental technician using conventional software.

METHODS

Thirty partial arch scans of prepared posterior teeth were used. The crowns were designed using two DL-based methods (AA and AD) and a technician-based method (NC). The crown design outcomes were three-dimensionally compared, focusing on tooth morphology, internal fit, occlusion, and proximal contacts, by calculating the geometric relationship. Statistical analysis utilized the independent t-test, Mann-Whitney test, one-way ANOVA, and Kruskal-Wallis test with post hoc pairwise comparisons (α = 0.05).

RESULTS

The AA and AD groups, with the NC group as a reference, exhibited no significant tooth morphology discrepancies across entire external or occlusal surfaces. The AD group exhibited higher root mean square and positive average values on the axial surface (P < .05). The AD and NC groups exhibited a better internal fit than the AA group (P < .001). The cusp angles were similar across all groups (P = .065). The NC group yielded more occlusal contact points than the AD group (P = .006). Occlusal and proximal contact intensities varied among the groups (both P < .001).

CONCLUSIONS

Crowns designed by using both DL-based software programs exhibited similar morphologies on the occlusal and axial surfaces; however, they differed in internal fit, occlusion, and proximal contacts. Their overall performance was clinically comparable to that of the technician-based method in terms of the internal fit and number of occlusal contact points.

CLINICAL SIGNIFICANCE

DL-based dental software for crown design can streamline the digital workflow in restorative dentistry, ensuring clinically-acceptable outcomes on tooth morphology, internal fit, occlusion, and proximal contacts. It can minimize the necessity of additional design optimization by dental technician.

A comprehensive review on artificial intelligence in water treatment for optimization. Clean water now and the future

Given the severe effects that toxic compounds present in wastewater streams have on humans, it is imperative that water and wastewater streams pollution be addressed globally. This review comprehensively examines various water and wastewater treatment methods and water quality management methods based on artificial intelligence (AI). Machine learning (ML) and AI have become a powerful tool for addressing problems in the real world and has gained a lot of interest since it can be used for a wide range of activities. The foundation of ML techniques involves training of a network with collected data, followed by application of learned network to the process simulation and prediction. The creation of ML models for process simulations requires measured data. In order to forecast and simulate chemical and physical processes such chemical reactions, heat transfer, mass transfer, energy, pharmaceutics and separation, a variety of machine-learning algorithms have recently been developed. These models have shown to be more adept at simulating and modeling processes than traditional models. Although AI offers many advantages, a number of disadvantages have kept these methods from being extensively applied in actual water treatment systems. Lack of evidence of application in actual water treatment scenarios, poor repeatability and data availability and selection are a few of the main problems that need to be resolved.

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.

AI and business management: Tracking future research agenda through bibliometric network analysis

This study has been designed to analyse the academic landscape of AI on the Scopus and Web of Science (WOS) indices and compare the findings. AI is one of the most prominent and preferred research areas, only a few studies are dedicated to the bibliometric aspect of it. There is a need to compare studies on AI over different databases to identify the impact and usefulness of those studies in decision-making in business management. To conduct this analysis, the authors have collected data from both Scopus and WOS. 'VOSviewer', 'R-Studio', and 'MS Excel' software have been used for performance analysis and science mapping. This is one of the exceptional studies which perform a comparative analysis between two indices and also identifies funding sponsors for support of research in AI. "Dwivedi, Y.K." is the most productive author and "Huang, Minghui" is the most impactful author. "National Natural Science Foundation of China" is the funding agency which has significantly supported AI research. Technical aspects like "Machine learning", "neural networks", and "blockchain" with 'Sustainability', 'sustainable development', 'accounting', and 'auditing' are trending themes for managerial decision-making.

A longitudinal study on artificial intelligence adoption: understanding the drivers of ChatGPT usage behavior change in higher education

As the field of artificial intelligence (AI) continues to progress, the use of AI-powered chatbots, such as ChatGPT, in higher education settings has gained significant attention. This paper addresses a well-defined problem pertaining to the critical need for a comprehensive examination of students' ChatGPT adoption in higher education. To examine such adoption, it is imperative to focus on measuring actual user behavior. While measuring students' ChatGPT usage behavior at a specific point in time can be valuable, a more holistic approach is necessary to understand the temporal dynamics of AI adoption. To address this need, a longitudinal survey was conducted, examining how students' ChatGPT usage behavior changes over time among students, and unveiling the drivers of such behavior change. The empirical examination of 222 Dutch higher education students revealed a significant decline in students' ChatGPT usage behavior over an 8 month period. This period was defined by two distinct data collection phases: the initial phase (T1) and a follow-up phase conducted 8 months later (T2). Furthermore, the results demonstrate that changes in trust, emotional creepiness, and Perceived Behavioral Control significantly predicted the observed change in usage behavior. The findings of this research carry significant academic and managerial implications, as they advance our comprehension of the temporal aspects of AI adoption in higher education. The findings also provide actionable guidance for AI developers and educational institutions seeking to optimize student engagement with AI technologies.

Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics

Over the period of the preceding decade, artificial intelligence (AI) has proved an outstanding performance in entire dimensions of science including pharmaceutical sciences. AI uses the concept of machine learning (ML), deep learning (DL), and neural networks (NNs) approaches for novel algorithm and hypothesis development by training the machines in multiple ways. AI-based drug development from molecule identification to clinical approval tremendously reduces the cost of development and the time over conventional methods. The COVID-19 vaccine development and approval by regulatory agencies within 1-2 years is the finest example of drug development. Hence, AI is fast becoming a boon for scientific researchers to streamline their advanced discoveries. AI-based FDA-approved nanomedicines perform well as target selective, synergistic therapies, recolonize the theragnostic pharmaceutical stream, and significantly improve drug research outcomes. This comprehensive review delves into the fundamental aspects of AI along with its applications in the realm of pharmaceutical life sciences. It explores AI's role in crucial areas such as drug designing, drug discovery and development, traditional Chinese medicine, integration of multi-omics data, as well as investigations into drug repurposing and polypharmacology studies.

AI-enabled organoids: Construction, analysis, and application

Organoids, miniature and simplified in vitro model systems that mimic the structure and function of organs, have attracted considerable interest due to their promising applications in disease modeling, drug screening, personalized medicine, and tissue engineering. Despite the substantial success in cultivating physiologically relevant organoids, challenges remain concerning the complexities of their assembly and the difficulties associated with data analysis. The advent of AI-Enabled Organoids, which interfaces with artificial intelligence (AI), holds the potential to revolutionize the field by offering novel insights and methodologies that can expedite the development and clinical application of organoids. This review succinctly delineates the fundamental concepts and mechanisms underlying AI-Enabled Organoids, summarizing the prospective applications on rapid screening of construction strategies, cost-effective extraction of multiscale image features, streamlined analysis of multi-omics data, and precise preclinical evaluation and application. We also explore the challenges and limitations of interfacing organoids with AI, and discuss the future direction of the field. Taken together, the AI-Enabled Organoids hold significant promise for advancing our understanding of organ development and disease progression, ultimately laying the groundwork for clinical application.

Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer

Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer.

Artificial Intelligence Readiness Among Jordanian Medical Students: Using Medical Artificial Intelligence Readiness Scale For Medical Students (MAIRS-MS)

BACKGROUND

Artificial intelligence (AI) application is increasingly used in all fields, especially, in medicine. However, for the successful incorporation of AI-driven tools into medicine, healthcare professional should be equipped with the necessary knowledge. From that, we aimed to assess the AI readiness among medical students in Jordan.

METHODS

A cross-sectional survey was conducted among medical students across 6 Jordanian universities. Prevalidated Medical Artificial Intelligence Readiness Scale for Medical Students questionnaire was used. The questionnaire was distributed through social media groups of students. SPSS v.27 was used for analysis.

RESULTS

A total of 858 responses were collected. The mean AI readiness score was 64.2%. Students scored more in the ability domain with a mean of 22.57. We found that academic performance (Grade point average) positively associated with overall AI readiness (P = .023), and prior exposure to AI through formal education or experience significantly enhances readiness (P = .009). In contrast, AI readiness levels did not significantly vary across different medical schools in Jordan. Notably, most students (84%) did not receive a formal education about AI from their schools.

CONCLUSION

Incorporation of AI education in medical curricula is crucial to close knowledge gaps and ensure that students are prepared for the use of AI in their future career. Our findings highlight the importance of preparing students to engage with AI technologies, and to be equipped with the necessary knowledge about its aspect.

Evaluation of the efficiency, trueness, and clinical application of novel artificial intelligence design for dental crown prostheses

OBJECTIVE

The unique structure of human teeth limits dental repair to custom-made solutions. The production process requires a lot of time and manpower. At present, artificial intelligence (AI) has begun to be used in the medical field and improve efficiency. This study attempted to design a variety of dental restorations using AI and evaluate their clinical applicability.

METHODS

Using inlay and crown restoration types commonly used in dental standard models, we compared differences in artificial wax-up carving (wax-up), artificial digital designs (digital) and AI designs (AI). The AI system was designed using computer calculations, and the other two methods were designed by humans. Restorations were made by 3D printing resin material. Image evaluations were compared with cone beam computed tomography (CBCT) by calculating the root mean squared error.

RESULTS

Surface truth results showed that AI (68.4 µm) and digital-designed crowns (51.0 µm) had better reproducibility. Using AI for the crown reduced the time spent by 400% (compared to digital) and 900% (compared to wax-up). Optical microscopic and CBCT images showed that AI and digital designs had close margin gaps (p < 0.05). The margin gap of the crown showed that the wax-up group was 4.1 and 4.3 times greater than those of the AI and digital crowns, respectively. Therefore, the utilization of artificial intelligence can assist in the production of dental restorations, thereby enhancing both production efficiency and accuracy.

SIGNIFICANCE

It is expected that the development of AI can contribute to the reproducibility, efficiency, and goodness of fit of dental restorations.

College Students' Employability, Cognition, and Demands for ChatGPT in the AI Era Among Chinese Nursing Students: Web-Based Survey

BACKGROUND

With the rapid development of artificial intelligence (AI) and the widespread use of ChatGPT, nursing students' artificial intelligence quotient (AIQ), employability, cognition, and demand for ChatGPT are worthy of attention.

OBJECTIVE

We aimed to investigate Chinese nursing students' AIQ and employability status as well as their cognition and demand for the latest AI tool-ChatGPT. This study was conducted to guide future initiatives in nursing intelligence education and to improve the employability of nursing students.

METHODS

We used a cross-sectional survey to understand nursing college students' AIQ, employability, cognition, and demand for ChatGPT. Using correlation analysis and multiple hierarchical regression analysis, we explored the relevant factors in the employability of nursing college students.

RESULTS

In this study, out of 1788 students, 1453 (81.30%) had not used ChatGPT, and 1170 (65.40%) had never heard of ChatGPT before this survey. College students' employability scores were positively correlated with AIQ, self-regulation ability, and their home location and negatively correlated with school level. Additionally, men scored higher on college students' employability compared to women. Furthermore, 76.5% of the variance was explained by the multiple hierarchical regression model for predicting college students' employability scores.

CONCLUSIONS

Chinese nursing students have limited familiarity and experience with ChatGPT, while their AIQ remains intermediate. Thus, educators should pay more attention to cultivating nursing students' AIQ and self-regulation ability to enhance their employability. Employability, especially for female students, those from rural backgrounds, and students in key colleges, deserves more attention in future educational efforts.

Analyzing ChatGPT adoption drivers with the TOEK framework

With the rapid advancements in AI technology and its growing impact on various aspects of daily life, understanding the factors that influence users' adoption intention becomes essential. This study focuses on the determinants affecting the adoption intention of ChatGPT, an AI-driven language model, among university students. The research extends the Technology-Organization-Environment (TOE) framework by integrating the concept of knowledge application. A cross-sectional research design was employed, gathering data through a survey conducted to university students. Structural equation modeling was used to analyze the data, aimed at examining the relationships between key determinants influencing adoption intention. The findings of this research indicate that factors such as network quality, accessibility, and system responsiveness contribute to satisfaction. Furthermore, satisfaction, organizational culture, social influence, and knowledge application significantly affect adoption intention. These findings offer both theoretical and practical implications.