A March 2024 survey found that more than 40 percent of adults in the United States did not know much about artificial intelligence (AI). Individuals between 18 and 44 years old were most aware of artificial intelligence (AI).

Introduction: The use of artificial intelligence (AI) in medical imaging and radiotherapy has been met with both scepticism and excitement. However, clinical integration of AI is already well-underway. Many authors have recently reported on the AI knowledge and perceptions of radiologists/medical staff and students however there is a paucity of information regarding radiographers. Published literature agrees that AI is likely to have significant impact on radiology practice. As radiographers are at the forefront of radiology service delivery, an awareness of the current level of their perceived knowledge, skills, and confidence in AI is essential to identify any educational needs necessary for successful adoption into practice.Aim: The aim of this survey was to determine the perceived knowledge, skills, and confidence in AI amongst UK radiographers and highlight priorities for educational provisions to support a digital healthcare ecosystem.Methods: A survey was created on Qualtrics® and promoted via social media (Twitter®/LinkedIn®). This survey was open to all UK radiographers, including students and retired radiographers. Participants were recruited by convenience, snowball sampling. Demographic information was gathered as well as data on the perceived, self-reported, knowledge, skills, and confidence in AI of respondents. Insight into what the participants understand by the term “AI” was gained by means of a free text response. Quantitative analysis was performed using SPSS® and qualitative thematic analysis was performed on NVivo®.Results: Four hundred and eleven responses were collected (80% from diagnostic radiography and 20% from a radiotherapy background), broadly representative of the workforce distribution in the UK. Although many respondents stated that they understood the concept of AI in general (78.7% for diagnostic and 52.1% for therapeutic radiography respondents, respectively) there was a notable lack of sufficient knowledge of AI principles, understanding of AI terminology, skills, and confidence in the use of AI technology. Many participants, 57% of diagnostic and 49% radiotherapy respondents, do not feel adequately trained to implement AI in the clinical setting. Furthermore 52% and 64%, respectively, said they have not developed any skill in AI whilst 62% and 55%, respectively, stated that there is not enough AI training for radiographers. The majority of the respondents indicate that there is an urgent need for further education (77.4% of diagnostic and 73.9% of therapeutic radiographers feeling they have not had adequate training in AI), with many respondents stating that they had to educate themselves to gain some basic AI skills. Notable correlations between confidence in working with AI and gender, age, and highest qualification were reported.Conclusion: Knowledge of AI terminology, principles, and applications by healthcare practitioners is necessary for adoption and integration of AI applications. The results of this survey highlight the perceived lack of knowledge, skills, and confidence for radiographers in applying AI solutions but also underline the need for formalised education on AI to prepare the current and prospective workforce for the upcoming clinical integration of AI in healthcare, to safely and efficiently navigate a digital future. Focus should be given on different needs of learners depending on age, gender, and highest qualification to ensure optimal integration.

Just 19 percent of respondents in a March 2024 survey among adults in the United States were knowledgeable about augmented reality (AR). Among those between 18 and 34 years of age, around 26 percent were knowledgeable about AR.

The study examines variables to assess teachers' preparedness for integrating AI into South African schools. The dataset on the Excel sheet consists of 42 columns. The first ten columns comprise demographic variables such as Gender, Years of Teaching Experience (TE), Age Group, Specialisation (SPE), School Type (ST), School Location (SL), School Description (SD), Level of Technology Usage for Teaching and Learning (LTUTL), Undergone Training/Workshop/Seminar on AI Integration into Teaching and Learning Before (TRAIN), and if Yes, Have You Used Any AI Tools to Teach Before (TEACHAI). Columns 11 to 42 contain constructs measuring teachers' preparedness for integrating AI into the school system. These variables are measured on a scale of 1 = strongly disagree to 6 = strongly agree.

AI Ethics (AE): This variable captures teachers' perspectives on incorporating discussions about AI ethics into the curriculum.

Attitude Towards Using AI (AT): This variable reflects teachers' beliefs about the benefits of using AI in their teaching practices. It includes their expectations of having a positive experience with AI, improving their teaching experience, and enhancing their participation in critical discussions through AI applications.

Technology Integration (TI): This variable measures teachers' comfort in integrating AI tools and technologies into lesson plans. It also assesses their belief that AI enhances the learning experience for students, their proactive efforts to learn about new AI tools, and the importance they place on technology integration for effective AI education.

Social Influence (SI): This variable examines the impact of colleagues, administrative support, peer discussions, and parental expectations on teachers' preparedness to incorporate AI into their teaching practices.

Technological Pedagogical Content Knowledge (TPACK): This variable assesses teachers' ability to use technology to facilitate AI learning. It includes their capability to select appropriate technology for teaching specific AI content, and bring real-life examples into lessons.

AI Professional Development (AIPD): This variable evaluates the impact of professional development training on teachers' ability to teach AI effectively. It includes the adequacy of these programs, teachers' proactive pursuit of further professional development opportunities, and schools' provision of such opportunities.

AI Teaching Preparedness (AITP): This variable measures teachers' feelings of preparedness to teach AI. It includes their belief that their teaching methods are engaging, their confidence in adapting AI content for different student needs, and their proactive efforts to improve their teaching skills for AI education.

Perceived Self-Efficacy to Teaching AI (PSE): This variable captures teachers' confidence in their ability to teach AI concepts, address challenges in teaching AI, and create innovative AI-related teaching materials.

IntroductionThe study investigates the integration of artificial intelligence (AI) in higher education (HE) and its impact on pre-service teachers at the University of Latvia (UL) by exploring pre-service teachers' perceptions of the benefits and challenges of AI in both their academic learning and their future professional roles as educators, particularly regarding the promotion of inclusive education.MethodsData was collected via an online survey of 240 pre-service teachers across various disciplines at the UL. The survey included demographic details, AI usage patterns, and perceived benefits and challenges. Responses were analyzed using descriptive statistics, Kruskal-Wallis H tests, Spearman's correlation, and thematic analysis.ResultsLess than half of the participants used AI in their studies, with many expressing ambivalence or opposition toward AI. Benefits included language assistance and accessibility to global knowledge, while challenges involved reduced critical thinking and concerns over plagiarism. Despite recognizing AI's potential to promote inclusivity, most pre-service teachers have not applied it in practice. No significant differences in AI perceptions were found based on age, gender, or study level.DiscussionThe findings highlight a low adoption rate of AI among pre-service teachers and a gap between theoretical recognition of AI's potential and its practical application, particularly for inclusion. The study emphasizes the need for HE institutions to enhance AI literacy and readiness among future teachers.ConclusionAI is underutilized by pre-service teachers in both HE learning and teaching environments, which has implications for teacher preparation programs that better integrate AI literacy and inclusive practices.

The advent of artificial intelligence (AI) technologies has emerged as a promising solution to enhance healthcare efficiency and improve patient outcomes. The objective of this study is to analyse the knowledge, attitudes, and perceptions of healthcare professionals in Pakistan about AI in healthcare. We conducted a cross-sectional study using a questionnaire distributed via Google Forms. This was distributed to healthcare professionals (e.g., doctors, nurses, medical students, and allied healthcare workers) working or studying in Pakistan. Consent was taken from all participants before initiating the questionnaire. The questions were related to participant demographics, basic understanding of AI, AI in education and practice, AI applications in healthcare systems, AI’s impact on healthcare professions and the socio-ethical consequences of the use of AI. We analyzed the data using Statistical Package for Social Sciences (SPSS) statistical software, version 26.0. Overall, 616 individuals responded to the survey while n = 610 (99.0%) of respondents consented to participate. The mean age of participants was 32.2 ± 12.5 years. Most of the participants (78.7%, n = 480) had never received any formal sessions or training in AI during their studies/employment. A majority of participants, 70.3% (n = 429), believed that AI would raise more ethical challenges in healthcare. In all, 66.4% (n = 405) of participants believed that AI should be taught at the undergraduate level. The survey suggests that there is insufficient training about AI in healthcare in Pakistan despite the interest of many in this area. Future work in developing a tailored curriculum regarding AI in healthcare will help bridge the gap between the interest in use of AI and training.

Dataset on Attitudes of Bangladeshi Medical Students Towards Artificial Intelligence in Medicine and RadiologyThis dataset is part of a study examining the knowledge, perceptions, and attitudes of Bangladeshi undergraduate medical students towards artificial intelligence (AI) applications in medicine and radiology. The data was collected through a web-based cross-sectional survey conducted between September 7 and October 14, 2024. A structured questionnaire was designed to assess various aspects, including demographic details, familiarity with AI technologies, sources of AI-related knowledge, and attitudes toward AI's role in healthcare.Key features of the dataset include:Demographics: Age, gender, and self-reported tech-savviness of participants.AI Awareness and Knowledge: Levels of familiarity with AI applications in radiology and medicine and understanding of underlying technologies.Sources of AI Information: Contributions from mass media, social media, formal education, and peer discussions.Attitudes Toward AI: Insights into perceptions of AI's impact on radiology and medicine, concerns about AI replacing physicians, and enthusiasm for AI integration into medical curricula.Statistical Analyses: Results of subgroup comparisons (e.g., by gender and tech-savviness) and Likert-scale responses.The dataset provides comprehensive information on 330 participants, offering valuable insights for researchers, educators, and policymakers interested in AI adoption in medical education and healthcare.Ethics Approval:The study received approval from the FSIT Research Ethics Committee, Daffodil International University (Code: DIU/DoR/2023/11/1). Participation was voluntary, with informed consent obtained.File Format:The dataset is available in XLSX, along with the questionnaire used for data collection.

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.

AI-Powered Rare Disease Diagnosis Market Outlook

According to our latest research, the AI-powered rare disease diagnosis market size reached USD 1.48 billion globally in 2024. The market is expected to expand at a compelling CAGR of 21.7% between 2025 and 2033, projecting a value of USD 11.53 billion by the end of the forecast period. This robust growth is primarily driven by the increasing adoption of artificial intelligence in healthcare, the urgent need for timely and accurate diagnosis of rare diseases, and the rising availability of large-scale genomic and clinical datasets. As per the latest research, advancements in AI algorithms and the integration of multi-modal data sources are further accelerating the market’s upward trajectory, making AI-powered solutions indispensable in rare disease diagnostics.

The primary growth factor for the AI-powered rare disease diagnosis market is the critical need for early and precise identification of rare diseases, which often present with complex and ambiguous symptoms. Traditional diagnostic methods can take years to yield conclusive results, leading to significant delays in treatment and increased patient morbidity. AI-powered solutions, leveraging advanced machine learning and deep learning techniques, are revolutionizing this landscape by rapidly analyzing vast and diverse datasets, including genomic, phenotypic, and clinical data. This enables clinicians to identify disease patterns and correlations that would otherwise remain undetected, significantly reducing diagnostic odysseys and improving patient outcomes. Furthermore, the continuous evolution of AI algorithms ensures that diagnostic accuracy improves over time, further cementing the role of AI in rare disease identification.

Another key driver is the growing collaboration between healthcare institutions, technology companies, and research organizations, which is fostering innovation and knowledge sharing in the AI-powered rare disease diagnosis market. The proliferation of electronic health records (EHRs), next-generation sequencing technologies, and biobanks has created a rich reservoir of data for AI systems to learn from. These collaborations are not only enhancing the training and validation of AI models but are also facilitating the development of standardized protocols and regulatory frameworks. Governments and non-profit organizations are increasingly funding initiatives aimed at rare disease research, further supporting the integration of AI-driven tools into clinical practice. This ecosystem of collaboration is essential for overcoming the inherent challenges of rare disease diagnostics, such as limited patient populations and data heterogeneity.

The market is also benefiting from the rising awareness among clinicians, patients, and policymakers regarding the potential of AI to transform rare disease diagnosis. Educational initiatives, conferences, and publications are helping bridge the knowledge gap and promote the adoption of AI-powered diagnostic tools. Regulatory authorities are gradually establishing clear guidelines for the validation and deployment of AI in healthcare, which is instilling confidence among end-users and accelerating market penetration. Additionally, the increasing prevalence of rare diseases globally, coupled with an aging population and the expansion of healthcare infrastructure in emerging economies, is creating a fertile ground for market growth. These factors, collectively, are propelling the AI-powered rare disease diagnosis market into a phase of unprecedented expansion.

From a regional perspective, North America currently dominates the AI-powered rare disease diagnosis market, owing to its advanced healthcare infrastructure, significant investments in AI research, and a high concentration of leading technology and pharmaceutical companies. Europe follows closely, supported by robust government initiatives and a strong focus on rare disease research. The Asia Pacific region is poised for the fastest growth during the forecast period, driven by increasing healthcare expenditure, rapid digital transformation, and rising awareness of rare diseases. Latin America and the Middle East & Africa are also expected to witness steady growth as AI adoption in healthcare gains momentum and access to diagnostic technologies improves.

Component Analysis

The component segment of the AI-powered rare disease diagnosis market is categorized into software, h

AI Clinical Genomics Interpretation Market Outlook

According to our latest research, the AI Clinical Genomics Interpretation market size reached USD 1.12 billion globally in 2024, reflecting robust adoption across healthcare and research institutions. The market is expected to grow at a CAGR of 21.3% from 2025 to 2033, reaching a forecasted value of USD 8.13 billion by 2033. This rapid expansion is primarily driven by the increasing integration of artificial intelligence (AI) into genomics workflows, aiming to enhance the accuracy, speed, and scalability of clinical genomic data interpretation. As per our latest research, the convergence of AI and genomics is fundamentally transforming personalized medicine, enabling healthcare providers to make more informed decisions based on complex genetic information.

One of the key growth factors propelling the AI Clinical Genomics Interpretation market is the rising prevalence of chronic diseases and rare genetic disorders. As the global population continues to age and the incidence of conditions such as cancer, cardiovascular diseases, and rare hereditary disorders increases, there is a growing demand for advanced diagnostic and prognostic solutions. AI-driven genomics interpretation platforms offer the capability to efficiently analyze vast amounts of genetic data, identify clinically relevant variants, and provide actionable insights for targeted therapies. This demand is further amplified by the ongoing advancements in next-generation sequencing (NGS) technologies, which have made genomic data generation more accessible and affordable for both clinical and research applications.

Another significant driver of market growth is the increasing focus on precision medicine initiatives across the globe. Governments, research organizations, and private entities are investing heavily in genomics research and the development of AI-powered tools to interpret complex genetic information. The integration of AI in clinical genomics interpretation not only reduces the time required for data analysis but also minimizes the risk of human error, leading to improved diagnostic accuracy and better patient outcomes. Furthermore, AI algorithms are continuously evolving to incorporate real-world evidence, electronic health records, and multi-omics data, thus enhancing the predictive power of genomics-based diagnostics and therapeutics.

The expanding adoption of AI-based genomics interpretation solutions among healthcare providers, research institutions, and diagnostic laboratories is also fueled by the growing need for scalable, interoperable, and cost-effective platforms. AI-driven tools enable clinicians and researchers to rapidly interpret ever-increasing volumes of genomic data, streamline workflows, and support clinical decision-making. Additionally, strategic collaborations among technology vendors, healthcare organizations, and academic institutions are accelerating the development and deployment of innovative AI genomics solutions. This collaborative ecosystem is fostering the creation of standardized databases, shared knowledge bases, and best practices that are essential for widespread adoption and regulatory compliance.

From a regional perspective, North America currently dominates the AI Clinical Genomics Interpretation market, accounting for the largest share due to its advanced healthcare infrastructure, significant investments in genomics research, and early adoption of AI technologies. Europe follows closely, driven by supportive regulatory frameworks and substantial funding for precision medicine initiatives. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by rising healthcare expenditure, expanding genomic research capabilities, and increasing awareness of personalized medicine. Latin America and the Middle East & Africa are gradually emerging as promising markets, supported by improving healthcare systems and growing investments in digital health.

Component Analysis

The Component segment of the AI Clinical Genomics Interpretation market is broadly categorized into Software and Services. Software solutions constitute the backbone of this market, offering a range of functionalities from genomic data management and variant annotation to clinical report generation and integration with electronic health records. These platforms leverage advanced machine learning algorithms and natura

Background: Artificial intelligence (AI) has recently surfaced as a research topic in dermatology and dermatopathology. In a recent survey, dermatologists were overall positive toward a development with an increased use of AI, but little is known about the corresponding attitudes among pathologists working with dermatopathology. The objective of this investigation was to make an inventory of these attitudes.Participants and Methods: An anonymous and voluntary online survey was prepared and distributed to pathologists who regularly analyzed dermatopathology slides/images. The survey consisted of 39 question divided in five sections; (1) AI as a topic in pathology; (2) previous exposure to AI as a topic in general; (3) applications for AI in dermatopathology; (4) feelings and attitudes toward AI and (5) self-reported tech-savviness and demographics. The survey opened on March 13, 2020 and closed on May 5, 2020.Results: Overall, 718 responders (64.1% females) representing 91 countries were analyzed. While 81.5% of responders were aware of AI as an emerging topic in pathology, only 18.8% had either good or excellent knowledge about AI. In terms of diagnosis classification, 42.6% saw strong or very strong potential for automated suggestion of skin tumor diagnoses. The corresponding figure for inflammatory skin diseases was 23.0% (Padj < 0.0001). For specific applications, the highest potential was considered for automated detection of mitosis (79.2%), automated suggestion of tumor margins (62.1%) and immunostaining evaluation (62.7%). The potential for automated suggestion of immunostaining (37.6%) and genetic panels (48.3%) were lower. Age did not impact the overall attitudes toward AI. Only 6.0% of the responders agreed or strongly agreed that the human pathologist will be replaced by AI in the foreseeable future. For the entire group, 72.3% agreed or strongly agreed that AI will improve dermatopathology and 84.1% thought that AI should be a part of medical training.Conclusions: Pathologists are generally optimistic about the impact and potential benefit of AI in dermatopathology. The highest potential is expected for narrow specified tasks rather than a global automated suggestion of diagnoses. There is a strong need for education about AI and its use within dermatopathology.

EUCA dataset description Associated Paper: EUCA: the End-User-Centered Explainable AI Framework Authors: Weina Jin, Jianyu Fan, Diane Gromala, Philippe Pasquier, Ghassan Hamarneh Introduction: EUCA dataset is for modelling personalized or interactive explainable AI. It contains 309 data points of 32 end-users' preferences on 12 forms of explanation (including feature-, example-, and rule-based explanations). The data were collected from a user study on 32 layperson participants in the Greater Vancouver city area in 2019-2020. In the user study, the participants (P01-P32) were presented with AI-assisted critical tasks on house price prediction, health status prediction, purchasing a self-driving car, and studying for a biological exam [1]. Within each task and for its given explanation goal [2], the participants selected and rank the explanatory forms [3] that they saw the most suitable. 1 EUCA_EndUserXAI_ExplanatoryFormRanking.csv Column description:

Index - Participants' number Case - task-explanation goal combination accept to use AI? trust it? - Participants response to whether they will use AI given the task and explanation goal require explanation? - Participants response to the question whether they request an explanation for the AI 1st, 2nd, 3rd, ... - Explanatory form card selection and ranking cards fulfill requirement? - After the card selection, participants were asked whether the selected card combination fulfill their explainability requirement.

2 EUCA_EndUserXAI_demography.csv It contains the participants demographics, including their age, gender, educational background, and their knowledge and attitudes toward AI. EUCA dataset zip file for download More Context for EUCA Dataset [1] Critical tasks There are four tasks. Task label and their corresponding task titles are: house - Selling your house car - Buying an autonomous driving vehicle health - Personal health decision bird - Learning bird species Please refer to EUCA quantatative data analysis report for the storyboard of the tasks and explanation goals presented in the user study. [2] Explanation goal End-users may have different goals/purposes to check an explanation from AI. The EUCA dataset includes the following 11 explanation goals, with its [label] in the dataset, full name and description

[trust] Calibrate trust: trust is a key to establish human-AI decision-making partnership. Since users can easily distrust or overtrust AI, it is important to calibrate the trust to reflect the capabilities of AI systems.

[safe] Ensure safety: users need to ensure safety of the decision consequences.

Background: Artificial intelligence (AI) has recently surfaced as a research topic in dermatology and dermatopathology. In a recent survey, dermatologists were overall positive toward a development with an increased use of AI, but little is known about the corresponding attitudes among pathologists working with dermatopathology. The objective of this investigation was to make an inventory of these attitudes.Participants and Methods: An anonymous and voluntary online survey was prepared and distributed to pathologists who regularly analyzed dermatopathology slides/images. The survey consisted of 39 question divided in five sections; (1) AI as a topic in pathology; (2) previous exposure to AI as a topic in general; (3) applications for AI in dermatopathology; (4) feelings and attitudes toward AI and (5) self-reported tech-savviness and demographics. The survey opened on March 13, 2020 and closed on May 5, 2020.Results: Overall, 718 responders (64.1% females) representing 91 countries were analyzed. While 81.5% of responders were aware of AI as an emerging topic in pathology, only 18.8% had either good or excellent knowledge about AI. In terms of diagnosis classification, 42.6% saw strong or very strong potential for automated suggestion of skin tumor diagnoses. The corresponding figure for inflammatory skin diseases was 23.0% (Padj < 0.0001). For specific applications, the highest potential was considered for automated detection of mitosis (79.2%), automated suggestion of tumor margins (62.1%) and immunostaining evaluation (62.7%). The potential for automated suggestion of immunostaining (37.6%) and genetic panels (48.3%) were lower. Age did not impact the overall attitudes toward AI. Only 6.0% of the responders agreed or strongly agreed that the human pathologist will be replaced by AI in the foreseeable future. For the entire group, 72.3% agreed or strongly agreed that AI will improve dermatopathology and 84.1% thought that AI should be a part of medical training.Conclusions: Pathologists are generally optimistic about the impact and potential benefit of AI in dermatopathology. The highest potential is expected for narrow specified tasks rather than a global automated suggestion of diagnoses. There is a strong need for education about AI and its use within dermatopathology.

Background: Artificial intelligence (AI) is used in ophthalmological disease screening and diagnostics, medical image diagnostics, and predicting late-disease progression rates. We reviewed all AI publications associated with macular edema (ME) research Between 2011 and 2022 and performed modeling, quantitative, and qualitative investigations.Methods: On 1st February 2023, we screened the Web of Science Core Collection for AI applications related to ME, from which 297 studies were identified and analyzed (2011–2022). We collected information on: publications, institutions, country/region, keywords, journal name, references, and research hotspots. Literature clustering networks and Frontier knowledge bases were investigated using bibliometrix-BiblioShiny, VOSviewer, and CiteSpace bibliometric platforms. We used the R “bibliometrix” package to synopsize our observations, enumerate keywords, visualize collaboration networks between countries/regions, and generate a topic trends plot. VOSviewer was used to examine cooperation between institutions and identify citation relationships between journals. We used CiteSpace to identify clustering keywords over the timeline and identify keywords with the strongest citation bursts.Results: In total, 47 countries published AI studies related to ME; the United States had the highest H-index, thus the greatest influence. China and the United States cooperated most closely between all countries. Also, 613 institutions generated publications - the Medical University of Vienna had the highest number of studies. This publication record and H-index meant the university was the most influential in the ME field. Reference clusters were also categorized into 10 headings: retinal Optical Coherence Tomography (OCT) fluid detection, convolutional network models, deep learning (DL)-based single-shot predictions, retinal vascular disease, diabetic retinopathy (DR), convolutional neural networks (CNNs), automated macular pathology diagnosis, dry age-related macular degeneration (DARMD), class weight, and advanced DL architecture systems. Frontier keywords were represented by diabetic macular edema (DME) (2021–2022).Conclusion: Our review of the AI-related ME literature was comprehensive, systematic, and objective, and identified future trends and current hotspots. With increased DL outputs, the ME research focus has gradually shifted from manual ME examinations to automatic ME detection and associated symptoms. In this review, we present a comprehensive and dynamic overview of AI in ME and identify future research areas.

The digital age is transforming education, especially now that generative AI expands the possibilities for students and teachers. This change requires critical reflection on the educational and social impact of technology and a rethinking of methods and priorities for advanced and sustainable interventions by educators (Williamson et al., 2020). Added to this is how the information landscape is undergoing a significant transformation too, meeting the spread of the Open Education concept and the growth of Open Educational Resources (OER). The Open approach seeks to promotes a culture of shared knowledge to remove barriers, leveraging digital technologies, and connecting formal and informal learning (Inamorato dos Santos et al., 2016).

Within this paradigm, teachers continue to play a key role especially in the quality of instruction and learning (Darling-Hammond et al., 2017). Improving their continuos training is widely recognized as a priority in international educational policies (OECD, 2019) and European strategies (European Council, 2020).

From this perspective, the ENCORE (ENriching Circular use of OeR for Education) Project emerges (https://project-encore.eu/ ). The ENCORE Approach is based on the idea of developing an innovative AI-based system to support the search and collection of high-quality OER to improve the teaching-learning process in a context of global challenges and lifelong learning (Raffaghelli et al., 2023). During the second part of the piloting of the ENCORE approach and platform, external workshops were organized for HE teachers, VET trainers and learners (external pilots). Eight external pilots were organized for the occasion, involving 226 participants. To collect data on the acceptance level of the tested instrument, the questionnaire used was inspired by a shortened version of the UTAUT model (Venkatesh et al., 2003; Kurelovic, 2020; Raffaghelli et al., 2022).

This dataset presents the 92 questionnaires collected by individual partners, already polished for study or use. The dataset introduces:

1. A codebook with relevant variables adopted to collect sample distribution data (Provenance and Institution) and inherent to the survey object (Area of Knowledge, Level of Usefulness, Intention to Use, ENCORE Best and Least and ENCORE improvements).
2. The procedures and questionnaire’s questions adopted.
3. The main dataset.

This dataset is complementary to the narrative reports produced by each institution and the final report to be constructed later.

References

Darling-Hammond, L., Hyler, M. E., & Gardner, M. (2017). Effective Teacher Professional Development. Learning Policy Institute, 1-76. https://learningpolicyinstitute.org/sites/default/files/productfiles/Effective_Teacher_Professional_Development_REPORT.pdf

European Council (2020). Council conclusions on “European teachers and trainers for the future”. Official Journal of the European Union. https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52020XG0609(02)&rid=5

Inamorato Dos Santos, A., Punie, Y., & Castaño, M. J. (2016). Opening up Education: A Support Framework for Higher Education Institutions. JRC Publications Repository. https://doi.org/10.2791/293408

Kurelovic, E. K. (2020). Acceptance of open educational resources driven by the culture of openness. In INTED2020 Proceedings (pp. 429-435). IATED

OECD (2019). TALIS 2018 Results (Vol. 1): Teachers and School Leaders as Lifelong Learners. OECD Publishing. https://doi.org/10.1787/1d0bc92a-en

Raffaghelli, J.E., Foschi, L.C., Crudele, F., Doria, B., Grion, V., & Cecchinato, G. (2023). The ENCORE Approach. Pedagogy of an AI-driven system to integrate OER in Higher Education & V ET. In ENCORE project results [Report]. ENCORE. https://www.research.unipd.it/handle/11577/3502320

Venkatesh, V., Morris, M.G., Davis, G.B., & Davis, F.D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly. https://doi.org/10.2307/30036540

Williamson, B., Eynon, R., & Potter, J. (2020). Pandemic politics, pedagogies and practices: Digital technologies and distance education during the coronavirus emergency. Learning, Media and Technology, 45(2). https://doi.org/10.1080/17439884.2020.1761641

Age-Related Hearing Loss Device Market was valued at USD 8.25 Billion in 2023 and is projected to reach USD 14.62 Billion by 2030, growing at a CAGR of 10% during the forecast period 2024-2030.

Global Age-Related Hearing Loss Device Market Drivers

Numerous variables impacting the market for age-related hearing loss devices are what propel its expansion and uptake. Here are a few significant market drivers:

Population Aging: The market for age-related hearing loss devices is mostly driven by the growing aging population worldwide. The need for hearing aids rises as people get older since age-related hearing loss is more common in this age group.

Age-Related Hearing Loss Prevalence: The demand for hearing aids is influenced by the high prevalence of presbycusis, or age-related hearing loss. Due to the widespread impact of this ailment on the aging population, there is a sizable market for hearing aids.

Technological Progress: Continuous technological developments in hearing aids, such as noise reduction, digital signal processing, and connectivity features, improve the functionality and user experience of devices intended for older adults with hearing loss. Innovation in technology propels market expansion.

Enhanced Accessibility and Conscience: More people are looking for and using hearing aids as a result of growing knowledge of the effects of age-related hearing loss and easier access to hearing healthcare services.

Increasing Costs of Healthcare: People may now invest in hearing healthcare, including buying hearing aids to treat age-related hearing loss, thanks to rising healthcare spending worldwide, particularly in industrialized nations.

Enhancing Policy for Reimbursement: Age-associated hearing loss devices are becoming more accessible and affordable because to improved reimbursement rules and coverage in certain healthcare systems for hearing aids and related services.

Discreet Designs Are Preferred by Customers: The preferences of people with age-related hearing loss are satisfied by the creation of smaller, discrete, and aesthetically pleasing hearing aids, which increases acceptance and adoption.

Artificial Intelligence (AI) Integration: Artificial intelligence (AI) has made it possible for hearing aids to incorporate cutting-edge features like personalized settings, adaptive learning, and enhanced speech recognition, which improve overall performance and user happiness.

Integrating wireless connectivity with smartphones: The industry is growing because hearing aids that integrate with smartphones and have wireless connectivity offer extra capabilities including customizable settings, remote control choices, and audio streaming straight to the devices.

Governmental Programs to Promote Hearing Health: The usage of age-related hearing loss devices is influenced by government programs and public health campaigns that emphasize the need of maintaining good hearing, particularly among the elderly.

Untreated Hearing Loss's Effect on Life Quality: The need for age-related hearing loss devices is being driven by people's growing awareness of the negative effects of untreated hearing loss on their quality of life and their rising search for solutions.

Device Personalization and Customization: Increased user satisfaction and market expansion are fueled by the trend toward personalization and customisation in hearing aids, which includes adjusting settings and creating customized solutions.

This INSPIRE award is jointly funded by the Information Integration and Informatics Program in the Information and Intelligent Systems Division of the Computer and Information Sciences Directorate, the Marine Geology and Geophysics Program in the Ocean Sciences Division of the Geosciences Directorate, the Arctic Natural Sciences Program in the Arctic Sciences Division and the Antarctic Glaciology Program in the Antarctic Sciences Division in the Office of Polar Programs, and the Office of Cyberinfrastructure. The critical first step in the analysis of paleoclimate records like ice or sediment cores is the construction of an age model, which relates the depth in a core to the calendar age of the material at that point. The reasoning involved in age-model construction is complex, subtle, and scientifically demanding because the processes that control the rate of material accumulation over time, and that affect the core between formation and sampling, are unknown. Geoscientists approach this problem by treating the core like a crime scene and asking the question: "What physical and chemical processes could have produced this situation, and what does that say about the timeline?" However, the sheer number of possibilities, coupled with the volume and complexity of the climatology data that is currently available and is continually collected, severely limit the scope of these investigations. The goal of this project is to remove this roadblock. This research will lead to an integrated software tool called CScibox, that uses automated reasoning techniques to help scientists analyze ice and sediment cores. It employs a cyberinfrastructure that provides powerful, intuitive tools on a scientist's desktop while taking full advantage of modern data- and computation-intensive computing and networking infrastructure -- including workflow-based computation, parallel execution, distributed systems, cluster machines and the cloud. CScibox will not only improve the ability of individual geoscientists analyze single cores; it has the potential to transform the field of paleoclimatology by facilitating rapid, reproducible analysis and synthesis of the information in the diverse collections of raw data available in data archives to foster understanding and improved scientific decision making. This will have broad impacts for society by allowing scientists to develop deeper insights into the roles of various factors in the complex relationships that give rise to geological records of the earth's climate that are used in today's models of environmental change. This project also has a broad educational impact. Students involved in the development and implementation of CScibox will develop skills in interdisciplinary research and will learn how to apply computational methodology in a challenging scientific context that has not yet significantly benefitted from developments in information technology. CScibox is designed to be easy to install and use; see the project web site (http://www.cs.colorado.edu/~lizb/cscience.html) for source code, documentation, and examples of its use. Future steps include extending the work to other paleoclimate data, working with geoscientists to make the user interface as intuitive as possible, and holding demos and workshops at geosciences conferences to educate that community about what the tool can do and how to use it. This project marries computer science and geoscience to address a critical roadblock in paleoclimate research: the construction of age models, which relate the distance along a temporal record (such as depth in a sediment or ice core) to the calendar age of the material at that point. The goal of the CScience project is to remove that roadblock. Its centerpiece is an integrated software tool called CScibox, which will employ artificial intelligence (AI) techniques to capture the knowledge of expert climatologists and help its users explore the complex hypotheses and large, heterogeneous digital datasets that are involved in age-model construction. Since over-automation of such a complex task is imprudent, CScibox will act as an intelligent assistant, iteratively working through scenarios under the guidance of its user. Its output will be a set of possible age models for a given core, together with a full description of the reasoning involved in their construction.

Intimate partner violence (IPV) is a serious social problem in Chile. Understanding the patterns of internalization and the motivations maintaining it is crucial to design optimal treatments that ensure adherence and completeness. This, in addition, is essential to prevent revictimization and improve the quality of life of both victims and their children.The present study analyzes the success of a psychological treatment offered by a Chilean foundation helping IPV victims. A database analysis containing 1,279 cases was performed applying classical statistics and artificial intelligence methods. The aim of the research was to search for cluster grouping and to create a classification model that is able to predict IPV treatment completeness. The main results demonstrate the presence of two main clusters, one including victims who completed the treatment (cluster 1) and a second one containing victims who did not complete the treatment (cluster 2). Cluster classification using an XGBoost model of the treatment completeness had an accuracy of 81%. The results showed that living with the aggressor, age and educational level had the greatest impact on the classification. Considering these factors as input variables allow for a higher precision on the treatment completeness prediction. To our knowledge, this is the first study performed in Chile that uses AI for cluster grouping and for analyzing the variables contributing to the success of an IPV victims’ treatment.

The German Internet Panel (GIP) is an infrastructure project. The GIP serves to collect data about individual attitudes and preferences which are relevant for political and economic decision-making processes.
The questionnaire contains numerous experimental variations in the survey instruments. For more information, see the study documentation.

Topics: Vignette experiment on the fair decision on the granting of bank loans or financial products to private individuals and acceptance of the type of decision; Vignette experiment on the fair decision on the early release of prisoners and acceptance of the type of decision; Vignette experiment on the fair decision on the hiring of new employees or the dismissal of employees in the probationary period and acceptance of the type of decision; Vignette experiment on the fair decision of the reduction of unemployment benefits or the awarding of support measures of job seekers and acceptance of the type of decision; ownership of smartphone, mobile phone, desktop computer/PC or laptop, tablet, eBook reader or none of the above; account with user name and password at selected providers (Google, Facebook, Twitter, Linkedin, Xing, at none of these providers); awareness of the following terms: Artificial intelligence, computer algorithms, machine learning, recommendation services, targeted/personalised advertising, none of these terms; knowledge of the purposes for which artificial intelligence technologies are used (advertising on social networks, curation of news on social networks, recommendations in online shops, recommendations on video streaming sites, ranking of results in search engines, responses from intelligent assistants, suggestions about potential partners on dating platforms, content of Wikipedia articles, website of a local restaurant, for other purposes, none of these purposes); concern about privacy in general; agreement with various statements about sharing data (I don´t mind sharing personal information because everyone does it nowadays, You can´t live in our modern world without sharing personal information, If you share personal information, you don´t know who sees it all, I don´t mind sharing my personal information if it gets me products and services I´d like); feeling sufficient control over personal information; type of internet activities in the last 3 months (e.g. searched for address, searched for information about products and services, etc.); trust in institutions (banks, private companies, judiciary, employment agencies); conjoint experiment to decide between two hypothetical parties with different attributes on the ideological position of each party and the parties´ positions on the issues of free market economy (trade between Germany and EU countries), trade barriers (trade between the EU and non-EU countries), freedom of movement (access of workers from EU countries to Germany) and immigration (access of workers from non-EU countries to the EU); experiment on the position of concrete parties on the topics of free market economy, trade barriers, freedom of movement, immigration and European unification; participation in the election to the European Parliament; actual or hypothetical voting decision in the hypothetical voting decision in the election to the European Parliament; opinion on European unification; experiment on trust in the results of four different surveys on the topic of European unification; interest in the topic of European unification; clear opinion on whether the process of European unification should be further advanced or not.

Occupational situation: employment status or (professional) activities; professional status of paid activity (employed, self-employed or helping family member); short-time work or leave with/without continued payment of wages of employed persons; amount of work short-time work of employed persons; amount of work in self-employed activity (more, about the same or less than before the start of the Corona pandemic, not working at all in my self-employed activity at the moment); Place of work of main activity (exclusively on site with employer or client, mainly on site with employer, occasionally in home office, about equally on site with employer and in home office, mainly in home office, occasionally with employer or client, exclusively in home office); probability of own unemployment in the next 12 months.

Impact of measures to contain the Corona pandemic in Germany (economic damage greater than societal benefit vs. societal benefit greater than economic damage); respondent´s monthly net income (grouped); household net income (grouped); satisfaction with selected areas of life (work and family life).

Demography: sex; age (year of birth, categorised); highest educational degree; highest professional qualification; marital status; household size; employment status; German citizenship; frequency of private internet usage; federal state.

Additionally coded were: Respondent ID; household ID,...