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This paper introduces a project to develop a digital academic writing tool at Leeds Beckett University (LBU). Essay X-ray is an interactive online tool designed to help students get to grips with the structure and style of academic writing and was developed using the Articulate Storyline 360 platform. The aim was to expand LBU's academic skills support for students tasked with essay assignments, especially at Level 4 (first year, undergraduate), enabling independent learning using a self-paced format available open access and 24/7. This would complement existing academic skills provision (one-to-ones, workshops, drop-ins, static online resources), with the interactive element facilitating active, hands-on learning (Lumpkin, Achen and Dodd, 2015). Following a successful development, review and rollout process, the utility of Essay X-ray as an independent learning tool but also as a classroom resource was reported by students and colleagues. Tentative talks about additional versions (Dissertation X-ray, Report X-ray) have taken place, indicating its potential for rollout to other subject areas and assessment types. Finally, in-house digital academic skills tools like Essay X-ray are posited as a potential response to the recent upsurge in Generative Artificial Intelligence (GenAI) tools. Essay X-ray requires users to think critically about essay structure, style and content to create their own original pieces of writing, thus responding to questions about the maintenance of academic integrity in a digital world. These features enable users to develop their essay writing skills, in contrast to passive engagement with a GenAI programme that merely writes an answer for them.

Artificial intelligence can support increasingly complex conversational interactions and also has the potential to interpret meanings from free text input and make recommendations based on patterns in data. There are important opportunities to apply this to real-world problems faced in access to education. In this paper, we summarise existing research and trends linking disability support and technology, then report on a survey conducted with students with disclosed disabilities (n = 138) to explore what systems might need to do to effectively understand disabled students in their own words, and provide suggestions of technologies, strategies and resources that could be relevant to overcoming barriers to learning. Through thematic analysis, five approaches that students used to talk about their disabilities are identified(medical, functional, support, experiential and administrative), and three major types of suggestions they make around what supported them and may be useful to others are also identified (external tools, university support and practices, concerns and solutions). The survey approach and the findings of the analysis provide a potential foundation for the effective design of systems that could crowdsource a knowledgebase around disabilities, hold conversations to understand disabilities and barriers and make relevant recommendations to individuals as to how they could overcome these barriers.

Cheating behaviors have been construed as a continuing and somewhat vexing issue for academic institutions as they increasingly conduct educational processes online and impose metrics on instructional evaluation. Research, development, and implementation initiatives on cheating detection have gained new dimensions in the advent of artificial intelligence (AI) applications; they have also engendered special challenges in terms of their social, ethical, and cultural implications. An assortment of commercial cheating-detection systems have been injected into educational contexts with little input on the part of relevant stakeholders. This paper expands several specific cases of how systems for the detection of cheating have recently been implemented in higher education institutions in the US and UK. It investigates how such vehicles as wearable technologies, eye scanning, and keystroke capturing are being used to collect the data used for anti-cheating initiatives, often involving systems that have not gone through rigorous testing and evaluation for their validity and potential educational impacts. The paper discusses accountability- and policy-related issues concerning the outsourcing of cheating detection in institutional settings in the light of these emerging technological practices as well as student resistance against the systems involved. The cheating-detection practices can place students in a disempowered, asymmetrical position that is often at substantial variance with their cultural backgrounds.

Purpose: This purpose of this paper is to report on the development of an evidence-informed framework created to facilitate the formulation of generative artificial intelligence (GenAI) academic integrity policy responses for English medium instruction (EMI) higher education, responding to both the bespoke challenges for the sector and longstanding calls to define and disseminate quality implementation good practice. Design/methodology/approach: A virtual nominal group technique engaged experts (n = 14) in idea generation, refinement and consensus building across asynchronous and synchronous stages. The resulting qualitative and quantitative data were analysed using thematic analysis and descriptive statistics, respectively. Findings: The GenAI Academic Integrity Policy Development Blueprint for EMI Tertiary Education is not a definitive mandate but represents a roadmap of inquiry for reflective deliberation as institutions chart their own courses in this complex terrain. Research limitations/implications: If repeated with varying expert panellists, findings may vary to a certain extent; thus, further research with a wider range of stakeholders may be necessary for additional validation. Practical implications: While grounded within the theoretical underpinnings of the field, the tool holds practical utility for stakeholders to develop bespoke policies and critically re-examine existing frameworks. Social implications: As texts produced by students using English as an additional language are at risk of being wrongly accused of GenAI-assisted plagiarism, owing to the limited efficacy of text classifiers such as Turnitin, the policy recommendations encapsulated in the blueprint aim to reduce potential bias and unfair treatment of students. Originality/value: The novel blueprint represents a step towards bridging concerning gaps in policy responses worldwide and aims to spark discussion and further much-needed scholarly exploration to this end.

The contribution of laboratory medicine in delivering value-based care depends on active cooperation and trust between pathologist and clinician. The effectiveness of medicine more in general depends in turn on active cooperation and trust between clinician and patient. From the second half of the 20th century, the art of medicine is challenged by the spread of artificial intelligence (AI) technologies, recently showing comparable performances to flesh-and-bone doctors in some diagnostic specialties. Being the principle source of data in medicine, the laboratory is a natural ground where AI technologies can disclose the best of their potential. In order to maximize the expected outcomes and minimize risks, it is crucial to define ethical requirements for data collection and interpretation by-design, clarify whether they are enhanced or challenged by specific uses of AI technologies, and preserve these data under rigorous but feasible norms. From 2018 onwards, the European Commission (EC) is making efforts to lay the foundations of sustainable AI development among European countries and partners, both from a cultural and a normative perspective. Alongside with the work of the EC, the United Kingdom provided worthy-considering complementary advice in order to put science and technology at the service of patients and doctors. In this paper we discuss the main ethical challenges associated with the use of AI technologies in pathology and laboratory medicine, and summarize the most pertaining key-points from the guidelines and frameworks before-mentioned., (© 2022 Walter de Gruyter GmbH, Berlin/Boston.)

ChatGPT could allow students to plagiarize the content of their coursework with little risk of detection. Little is known about undergraduate willingness to use AI tools. In this study, psychology undergraduates (N = 160) from the United Kingdom, indicated their willingness to use, and history of using, ChatGPT to write university assignments. Almost a third (32%) indicated that they would use such tools; 15% indicated that they had used them already. Neither personality (conscientiousness, agreeableness, Machiavellianism, narcissism), academic performance, nor study skills self-efficacy could predict future use of AI tools. A novel Degree Apathy Scale was the only significant predictor. Willingness to use AI tools was greater when the risk of getting caught was low, and punishment was light, particularly for those high in degree apathy. Findings suggest that degree apathy is a key risk factor in academic misconduct. Wider research and pedagogical applications of degree apathy are discussed. • Thirty-two percent of students surveyed reported that they would use ChatGPT on future academic assignments. • Future use of AI tools was significantly predicted by a new Degree Apathy Scale. • Big Five and Dark Triad scores were not significant predictors, nor was previous academic performance. • Likelihood of cheating using ChatGPT was reduced with increasing risk of detection or severity of punishment. [ABSTRACT FROM AUTHOR]

While ChatGPT has recently become very popular, AI has a long history and philosophy. This paper intends to explore the promises and pitfalls of the Generative Pre-trained Transformer (GPT) AI and potentially future technologies by adopting a speculative methodology. Speculative future narratives with a specific focus on educational contexts are provided in an attempt to identify emerging themes and discuss their implications for education in the 21st century. Affordances of (using) AI in Education (AIEd) and possible adverse effects are identified and discussed which emerge from the narratives. It is argued that now is the best of times to define human vs AI contribution to education because AI can accomplish more and more educational activities that used to be the prerogative of human educators. Therefore, it is imperative to rethink the respective roles of technology and human educators in education with a future-oriented mindset.

Language testing is witnessing increasing interest in the potential for AI to support test development and validation. To date, published research involving AI in language testing has typically been conducted in the context of high-stakes proficiency tests and the potential for this technology to support local language testing is under-researched. The current study seeks to address this gap by reporting on the piloting of an AI generated language test in the context of a university in the UK. The focus of the paper was selected to explore two key areas in the work of J.D. Brown, namely pragmatics and connected speech. In the study, international students with English as a second language completed an AI generated test of pragmatics created from a transcript of spontaneous interaction, and a test-taking strategy questionnaire. Results demonstrated that the test did not provide a sufficiently reliable measurement of test takers' pragmatic competence, although reliability estimates did improve with the removal of problematic items. Implications for language test development involving AI are discussed.

Purpose: There is limited scholarship on artificial intelligence (AI) in higher education governance, despite the growing prevalence of AI-powered technologies in many fields, including education. However, as the technology is still nascent and has yet to reach its full potential, ideas and arguments abound, championing or cautioning against the use of these technologies. Design/Approach/Methods: To fill this gap in research on policy networks and AI in British higher education, this article employs network ethnography and discourse analysis to study how ideas about AI-powered technologies in higher education circulate in policy networks in the United Kingdom. Findings: The findings evidence a policy network showing signs of a heterarchy permeated by neoliberal rationales and populated by policy actors actively promoting artificial intelligence technologies to be used in education. Originality/Value: This paper builds on existing research by looking at the university and not-for-profit sectors, in addition to the governmental and educational technology sectors. Using network ethnography, this article expands our understanding of the policy actors involved and critically analyzes ideas regarding the use of AI in education.

The emergence of ChatGPT, a Generative AI program, has sparked discussions about its teaching and learning value, and concerns about academic integrity in higher education (HE). An extant review of the literature indicates that a scarcity of research exists on GenAI, specifically a synthesis of the official views, guidelines and articles of top-ranked universities on the use, limitations, challenges, and opportunities brought by ChatGPT in the early phase when ChatGPT was released in 2022 until early May 2023, which can offer insights into the concerns and recommendations for educators. Using the corpus assisted discourse analysis approach, this study identified the key themes and emotions elicited by evaluations of the ChatGPT situation from a self-built corpus containing 151 articles from 47 top-ranked U.S. universities and 34 U.K. ones. Our findings indicated three prominent themes discussed on official websites, including ChatGPT as a text and content generator, use of ChatGPT in teaching and learning, and potential implications and opportunities of using ChatGPT in HE. Further examination revealed that bias, concern, worry, threat, fear, and trust were the prevailing emotions relating to ChatGPT. Illustrated with examples collected from our corpus, this paper offers an in-depth discussion of universities' first responses to the use of ChatGPT. The insights gained have some pedagogical implications for academics, researchers and educators and may inform policy and practice in HE on the use of GenAI.

This study investigates the influence of generative artificial intelligence (GAI), specifically AI text generators (ChatGPT), on critical thinking skills in UK postgraduate business school students. Using Bloom's taxonomy as theoretical underpinning, we adopt a mixed-method research employing a sample of 107 participants to investigate both the influence and challenges of these technologies in higher education. Our findings reveal that the most significant improvements occurred at the lower levels of Bloom's taxonomy. We identify concerns relating to reliability, accuracy, and potential ethical implications of its application in higher education. The significance of this paper spans across, pedagogy, policy and practice, offering insights into the complex relationship between AI technologies and critical thinking skills. While highlighting the multifaceted aspects of the impact of AI in education, this article serves as a guide to educators and policymakers, stressing the importance of a comprehensive approach to fostering critical thinking and other transferable skills in the higher education landscape.

For over two decades now, the application of Citizen Science to Education has been evolving, and fundamental topics, such as the drivers of motivation to participate in Citizen Science projects, are still under discussion. Some recent developments, though, like the use of Artificial Intelligence to support data collection and validation, seem to point to a clear-cut divergence from the mainstream research path. The objective of this paper is to summarise the development trajectory of research on Citizen Science in Education so far, and then shed light on its future development, to help researchers direct their efforts towards the most promising open questions in this field. We achieved these objectives by using the lens of the Affordance-Actualisation theory and the Main Path Analysis method.

This is a conceptual paper that examines the origin and development of universities and their current role in global society. There has been an unprecedented and exponential growth of technology and artificial intelligence capabilities over the past ten years which is challenging current working practices and affecting all areas of society. The paper examines how this role may change to match the new demands placed on them by a digitally enabled society that has greater leisure time. The design of the paper is first to detail some of the changes in work practices that are taking place and how these will impact on society. It then offers several ways in which universities could modify their role to respond to these emerging challenges. This could include new courses, new organisational structures and new pedagogical practices. The paper provides a platform for discussion and debate around the strategic vision and direction of travel for higher education.

Purpose: This paper aims to present a comprehensive review of the present state and trends of smart education research. It addresses the need to have a systematic review of smart education to depict its research landscape in view of the growing volume of related publications. Design/methodology/approach: A bibliometric analysis of publications on smart education published in 2011 to 2020 was conducted, covering their patterns and trends in terms of collaboration, key publications, major topics and trends. A total of 1,317 publications with 29,317 cited references were collected from the Web of Science and Scopus for the bibliometric analysis. Findings: Research on smart education has been widely published in various sources. The most frequently cited references are all theoretical or discussion articles. Researchers in the USA, China, South Korea, India and Russia have been most active in research collaborations. However, international collaborations have remained infrequent except for those involving the USA. The research on smart education broadly covered smart technologies as well as teaching and learning. The emerging topics have addressed areas such as the Internet of Things, big data, flipped learning and gamification. Originality/value: This study depicts the intellectual landscape of smart education research, and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and research needs, and suggest future work related to research collaborations on a larger scale and more studies on smart pedagogies.

Purpose: Foresight J's journey started in 1999, and in 2022, it marked the conclusion of its 24 years of publication. This paper aims to provide an overall overview of important research trends published in Foresight J between 1999 and 2022 by conducting a quantitative analysis of the journal's literature. The overarching goal is to provide valuable insights into the dynamics of scholarly communication, aiding researchers, institutions and policymakers in assessing the significance and influence of academic work, guiding future research directions and academic evaluation. Design/methodology/approach: The two bibliometrics methodologies that make up the methodology of this article are scientific mapping and performance analysis. Authors have explained the development and composition of the Foresight J using these methods. The SCOPUS database is being used in current research to analyse several dimensions, such as the evolution of publications by year, the most cited papers, core authors and researchers, leading countries and prolific institutions. Moreover, the conceptual structure, scope, burst detection and co-occurrence analysis of the journal are mapped using network visualization software such as VOSviewer, CiteSpace and RStudio. Findings: With a strong track record of output over the years, Foresight J has continued to develop in terms of publications. It is determined that "Saritas" is the author with the greatest overall impact. However, according to SCOPUS bibliometric data, "Blackman" and "Richardson" are the authors with the greatest relevance in terms of the quantity of articles. In addition, it becomes apparent that the USA, Australia and the UK are very productive nations in terms of publications. The most popular fields of the journal have always been forecasting, foresight, scenario planning, strategic planning, decision-making, technology and sustainable development. These are also the author keywords that appear the most frequently. In contrast, new study themes in the Foresight J include digital technologies, innovation, sustainability, blockchain, artificial intelligence and sustainability. Research limitations/implications: Several noteworthy research implications are provided by the bibliometric study of Foresight J. "Saritas" is the author with the most overall impact, indicating that the precise contributions and influence of this researcher in the fields of forecasting, foresight and related fields. Given that "Blackman" and "Richardson" are well-known writers, it is also critical to examine the scope and complexity of their contributions to potentially identify recurring themes or patterns in their writing. The geographic productivity results, which show that the USA, Australia and the UK are the top three countries for Foresight J publications, may encourage more research into regional differences, patterns of collaboration and the worldwide distribution of research endeavours in the context of forecasting and foresight. Popular fields including scenario planning, forecasting, foresight and sustainable development are consistent, indicating persistent research interests. Examining the causes of these subjects' ongoing relevance can reveal information about the consistency and development of scholarly interests over time. Practical implications: Foresight J's bibliometric analysis has real-world applications for many stakeholders. It helps editors and publishers make strategic decisions about outreach and content by providing insights regarding the journal's influence. Assessing organizational and author productivity helps institutions allocate resources more effectively. Policymakers acquire an instrument to evaluate research patterns and distribute funds efficiently. In general, bibliometric study of a journal helps decisionmakers in academic publishing make well-informed choices that maximize the potential of options for authors, editors, institutions and policymakers. Social implications: The societal ramifications of bibliometrically analysing Foresight J from 1999 and 2022 are substantial. This analysis highlights, over the past 24 years, research trends, technological developments and societal priorities have changed by methodically looking through the journal's articles. Gaining knowledge about the academic environment covered by the journal can help raise public awareness of important topics and promote critical thinking. In addition, the analysis can support evidence-based decision-making by alerting decision makers to the influential research that was published in Foresight J. This could have an impact on the course of policies pertaining to innovation, technology and societal development. Originality/value: This study presents a first comprehensive article that provides a general overview of the main trends and patterns of the research over the Foresight J's history since its inception. Also, the paper will help the scientific community to know the value and impact of Foresight J. [ABSTRACT FROM AUTHOR]

In this paper, 1,538 papers retrieved with the keywords "sports artificial intelligence (AI)" on the Web of Science database since 2007 were taken as the data source, and the Cite Space V software was used to visualize and analyze them. A visual knowledge graph was used to streamline the countries, institutions and authors conducting sports AI research, discipline distribution, research hotspots and development trends in the past 15 years. Subsequently, its development direction and research progress were discussed. Sports AI was widely distributed, with the US, China and the UK leading the way. The most prolific authors and teams in research on sports AI were concentrated in American universities. Their main research direction is to develop and improve smart wearable devices based on machine learning and deep learning technologies for different groups of people. Research on sports AI involved multiple disciplines, which mainly applied and referred to research methodologies and theories on engineering, computer science and sports science. It could be seen from the frequency and centrality of keywords that in the current field of sports AI, machine learning is the main direction, artificial neural networks is the main algorithm, and practical and empirical research based on data mining is the focus. The research hotspots were divided into three major clusters: physical health promotion, sports injury prevention and control, and athletic performance enhancement. How to introduce intelligent technology into sports for a perfect integration still has an arduous and long way to go. Future development requires joint efforts and participation of scientific researchers, professionals and common people. [ABSTRACT FROM AUTHOR]

The adoption of data science brings vast benefits to Small and Medium-sized Enterprises (SMEs) including business productivity, economic growth, innovation and job creation. Data science can support SMEs to optimise production processes, anticipate customers' needs, predict machinery failures and deliver efficient smart services. Businesses can also harness the power of artificial intelligence (AI) and big data, and the smart use of digital technologies to enhance productivity and performance, paving the way for innovation. However, integrating data science decisions into an SME requires both skills and IT investments. In most cases, such expenses are beyond the means of SMEs due to their limited resources and restricted access to financing. This paper presents trends and challenges towards effective data-driven decision making for organisations based on a 3-year long study which covered more than 85 UK SMEs, mostly from the West Midlands region of England. In particular, this study attempts to find answers to several key research questions around data science and AI adoption among UK SMEs, and the advantages of digitalisation and data-driven decision making, as well as the challenges hindering their effective utilisation of these technologies. We also present two case studies that demonstrate the potential of digitisation and data science, and use these as examples to unveil challenges and showcase the wealth of currently available opportunities for SMEs. [ABSTRACT FROM AUTHOR]

This study reviewed the status quo of research on machine learning (ML), including deep learning, in the rail industry. This study conducted a scientometric analysis and critical review of 640 papers selected from 12,675 web-crawled papers. The extensive and complex networks of topics, researchers, and countries were analyzed using the Louvain method, a co-occurrence keyword analysis, a degree centrality analysis, and other network analysis methods. The results indicate that the majority of studies of ML in the rail industry focused on maintenance activities and traffic management, and mainly targeted rolling stock, rails, and passengers. Overhead contact systems, including catenaries, are a high-demand objective for ML-based maintenance. Although analyses of tunnels and stations remain rare, passenger flow prediction, station air quality estimation, shield tunneling performance improvement, and ground settlement are areas of high importance. Geographically, China, the US, and the United Kingdom lead ML studies in the rail industry, and the level of collaboration is higher among European countries than among countries on other continents. Future challenges include ensuring the security and stability of ML, along with considering novel mindsets, the black-box effect, improvements in ML techniques, and resource overload when introducing ML technologies. [ABSTRACT FROM AUTHOR]

This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors' experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure. [ABSTRACT FROM AUTHOR]

Introduction: Missed fractures are the most frequent diagnostic error attributed to clinicians in UK emergency departments and a significant cause of patient morbidity. Recently, advances in computer vision have led to artificial intelligence (AI)-enhanced model developments, which can support clinicians in the detection of fractures. Previous research has shown these models to have promising effects on diagnostic performance, but their impact on the diagnostic accuracy of clinicians in the National Health Service (NHS) setting has not yet been fully evaluated., Methods and Analysis: A dataset of 500 plain radiographs derived from Oxford University Hospitals (OUH) NHS Foundation Trust will be collated to include all bones except the skull, facial bones and cervical spine. The dataset will be split evenly between radiographs showing one or more fractures and those without. The reference ground truth for each image will be established through independent review by two senior musculoskeletal radiologists. A third senior radiologist will resolve disagreements between two primary radiologists. The dataset will be analysed by a commercially available AI tool, BoneView (Gleamer, Paris, France), and its accuracy for detecting fractures will be determined with reference to the ground truth diagnosis. We will undertake a multiple case multiple reader study in which clinicians interpret all images without AI support, then repeat the process with access to AI algorithm output following a 4-week washout. 18 clinicians will be recruited as readers from four hospitals in England, from six distinct clinical groups, each with three levels of seniority (early-stage, mid-stage and later-stage career). Changes in the accuracy, confidence and speed of reporting will be compared with and without AI support. Readers will use a secure web-based DICOM (Digital Imaging and Communications in Medicine) viewer (www.raiqc.com), allowing radiograph viewing and abnormality identification. Pooled analyses will be reported for overall reader performance as well as for subgroups including clinical role, level of seniority, pathological finding and difficulty of image., Ethics and Dissemination: The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved on 13 December 2022). The use of anonymised retrospective radiographs has been authorised by OUH NHS Foundation Trust. The results will be presented at relevant conferences and published in a peer-reviewed journal., Trial Registration Numbers: This study is registered with ISRCTN (ISRCTN19562541) and ClinicalTrials.gov (NCT06130397). The paper reports the results of a substudy of STEDI2 (Simulation Training for Emergency Department Imaging Phase 2)., Competing Interests: Competing interests: JV and DJ of the Steering Committee are employees of Gleamer SAS, France. SA is a shareholder of RAIQC, UK. All other authors declare no competing interests., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

As the penetration of renewable energy sources into a power system increases, the significance of precise short-term forecasts for wind power generation becomes paramount. However, the erratic and non-periodic nature of wind poses challenges in accurately predicting the output. This paper presents a comprehensive investigation into forecasting wind power generation for the following day, using three machine learning models: long short-term memory (LSTM), convolutional neural network-bidirectional LSTM (CNN-biLSTM), and light gradient boosting machine (LGBM). In addition, this paper proposes a method to improve the prediction performance of LGBM by separating data according to the distribution of features, and training and testing each separated dataset with a distinct model. This study includes a comparative analysis of the performance of the proposed models in predicting wind turbine output, offering valuable insights into their respective efficiencies. The results of this investigation were analyzed for two geographically distinct wind farms (Korea and the UK). The findings of this study are expected to facilitate the selection of efficient prediction models within the forecast accuracy auxiliary service market and assist grid operators in ensuring reliable power supply for the grid. [ABSTRACT FROM AUTHOR]

Purpose: Artificial intelligence (AI), machine learning (ML) and deep learning (DL) are having a major impact on banking (FinTech), health (HealthTech), law (RegTech) and other sectors such as charitable fundraising (CharityTech). The pace of technological innovation and the ability of AI systems to think like human beings (simulate human intelligence), perform tasks independently, develop intelligence based on its own experiences and process layers of information to learn ever-complex representations of data (ML/DL) means that improvements in the rates at which this technology can undertake complex, technical and time-consuming tasks, identify people, objects, voices, patterns, etc., screen for 'problems' earlier, and provide solutions, provide astounding benefit in economic, political and social terms. The purpose of this paper is to explore advents in AI, ML and DL in the context of the regulatory compliance challenge faced by financial institutions in the United Kingdom (UK). Design/methodology/approach: The subject is explored through the analysis of data and domestic and international published literature. The first part of the paper summarises the context of current regulatory issues, the advents in deep learning, how financial institutions are currently using AI, and how AI could provide further technological solutions to regulatory compliance as of February 2023. Findings: It is suggested that UK financial institutions can further utilise AI, ML and DL as part of an armoury of solutions that ease the regulatory burden and achieve high levels of compliance success. Originality/value: To the best of the author's knowledge, this is the first study to specifically explore how AI, ML and DL can continue to assist UK financial institutions in meeting the regulatory compliance challenge and the opportunities provided for financial institutions by the metaverse. [ABSTRACT FROM AUTHOR]

Chatbots are increasingly being applied in the context of health care, providing access to services when there are constraints on human resources. Simple, rule-based chatbots are suited to high-volume, repetitive tasks and can therefore be used effectively in providing users with important health information. In this Viewpoint paper, we report on the implementation of a chatbot service called Ask Anxia as part of a wider provision of information and support services offered by the UK national charity, Anxiety UK. We reflect on the changes made to the chatbot over the course of approximately 18 months as the Anxiety UK team monitored its performance and responded to recurrent themes in user queries by developing further information and services. We demonstrate how corpus linguistics can contribute to the evaluation of user queries and the optimization of responses. On the basis of these observations of how Anxiety UK has developed its own chatbot service, we offer recommendations for organizations looking to add automated conversational interfaces to their services., (©Luke Collins, Niamh Nicholson, Nicky Lidbetter, Dave Smithson, Paul Baker. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 17.06.2024.)

Background: Medical Imaging and radiotherapy (MIRT) are at the forefront of artificial intelligence applications. The exponential increase of these applications has made governance frameworks necessary to uphold safe and effective clinical adoption. There is little information about how healthcare practitioners in MIRT in the UK use AI tools, their governance and associated challenges, opportunities and priorities for the future., Methods: This cross-sectional survey was open from November to December 2022 to MIRT professionals who had knowledge or made use of AI tools, as an attempt to map out current policy and practice and to identify future needs. The survey was electronically distributed to the participants. Statistical analysis included descriptive statistics and inferential statistics on the SPSS statistical software. Content analysis was employed for the open-ended questions., Results: Among the 245 responses, the following were emphasised as central to AI adoption: governance frameworks, practitioner training, leadership, and teamwork within the AI ecosystem. Prior training was strongly correlated with increased knowledge about AI tools and frameworks. However, knowledge of related frameworks remained low, with different professionals showing different affinity to certain frameworks related to their respective roles. Common challenges and opportunities of AI adoption were also highlighted, with recommendations for future practice., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Rationale and Objectives: Chat Generative Pre-trained Transformer (ChatGPT) is an artificial intelligence (AI) tool which utilises machine learning to generate original text resembling human language. AI models have recently demonstrated remarkable ability at analysing and solving problems, including passing professional examinations. We investigate the performance of ChatGPT on some of the UK radiology fellowship equivalent examination questions., Methods: ChatGPT was asked to answer questions from question banks resembling the Fellowship of the Royal College of Radiologists (FRCR) examination. The entire physics part 1 question bank (203 5-part true/false questions) was answered by the GPT-4 model and answers recorded. 240 single best answer questions (SBAs) (representing the true length of the FRCR 2A examination) were answered by both GPT-3.5 and GPT-4 models., Results: ChatGPT 4 answered 74.8% of part 1 true/false statements correctly. The spring 2023 passing mark of the part 1 examination was 75.5% and ChatGPT thus narrowly failed. In the 2A examination, ChatGPT 3.5 answered 50.8% SBAs correctly, while GPT-4 answered 74.2% correctly. The winter 2022 2A pass mark was 63.3% and thus GPT-4 clearly passed., Conclusion: AI models such as ChatGPT are able to answer the majority of questions in an FRCR style examination. It is reasonable to assume that further developments in AI will be more likely to succeed in comprehending and solving questions related to medicine, specifically clinical radiology., Advances in Knowledge: Our findings outline the unprecedented capabilities of AI, adding to the current relatively small body of literature on the subject, which in turn can play a role medical training, evaluation and practice. This can undoubtedly have implications for radiology., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

As both artificial intelligence (AI) and creativity are being foregrounded in UK policy agendas, this paper identifies a striking underrepresentation of artists and artistic practice in cultural policy discussing creative innovation. This is despite increasing academic literature, arts-led research, and case studies evidencing a close and dialogic relationship between art and AI. To illustrate this, we first call attention to the impact artistic practice has on AI, against the more common discourse of AI's impact on the arts. We then review UK policy addressing the intersection of the cultural sector, creative industries, and digital sector. Taking this context into account, we argue that artists and artistic practice are currently underrepresented in cultural policy advocating for investment in creative innovation. We suggest this under-acknowledgement is embedded as foundationally as the policy language used to articulate the intersection of arts and technologies, foregrounded by the semantic separation of "Visual arts" and "Artistic creation" from the "Digital Sector" in UK Standard Industrial Classifications. This separation reveals a misalignment of policy and practice that risks underrepresenting the important contribution artists make to the development of AI, and discourse around its role in society. Addressing this misalignment requires a review of policy language used to articulate the intersection of the cultural sector, creative industries, and digital sector in order to more closely align artistic practice with the development of AI. This is an important first step in establishing cultural policy that recognises, prioritises, and invests in artists as the agents of creative innovation that literature and practice evidence them to be. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has profoundly impacted every aspect of our lives, from economic to the social facets of contemporary society. While the new COVID-19 waves may not be anticipated to be as severe as previous ones, it would be unreasonable to assume that they will cease any time soon. Consequently, forecasting the number of future infections, recovered patients, and death cases remains a very much logical step in trying to fight against further waves, in conjunction with ongoing vaccination efforts. In this paper, we investigate the efficiency of three intelligent machine learning algorithms, namely GMDH, Bi-LSTM, and GA + NN, for COVID-19 forecasting, with an application to Iran and the United Kingdom. The experimental results show that the algorithms can be used to forecast the next six months of COVID-19 in terms of confirmed, recovered, and death cases, which gives a more ample timeframe for using the results to make better practical yet strategic decisions and take appropriate actions or measures to deploy resources effectively to contain or curb the spread of the coronavirus. Despite the distinct dynamics observed in the data, our analysis proves the robustness of the employed models. [ABSTRACT FROM AUTHOR]

This paper proposes a structured, tiered framework through a UNCITRAL Model Law to gradually integrate artificial intelligence (AI) into foreign investment law arbitration in an ethical and effective manner. It also explores the critical role arbitration institutions can play in facilitating AI implementation. It details how AI can assist arbitrators by searching vast datasets, automating routine tasks, and enhancing decision-making through analysis of previous cases. Current AI regulations in regions like the EU, UK, and Canada fall short in addressing the complexities of cross-border arbitration and ensuring interoperability. By emphasising the distinct contributions of the proposed UNCITRAL Model Law and arbitration institutions, the paper highlights a multi-faceted strategy to overcome challenges posed by outdated international conventions, inconsistencies in bilateral investment treaties, and the lack of comprehensive guidance. This approach aims to refine the integration of AI in arbitration processes, enhancing efficiency, fairness, and the legitimacy of the arbitration system. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to highlight the risks in using artificial intelligence (AI) as part of the hiring process, particularly focusing on the risks of discrimination when used. Design/methodology/approach: The approach taken was to draw upon current experiences of AI used in hiring processes through case studies and existing research and to discuss current and upcoming legislative measures in place within the UK and EU to extract key advice that companies can use to ensure AI is used as effectively and fairly as possible in the hiring process. Findings: This paper concludes that companies interested in using AI in the hiring process must proceed with caution and must be wary of the potential pitfalls in using it, as well as taking the necessary steps to avoid any risks of discrimination. Originality/value: The originality of this piece revolves around the authors' discussion of legal implications within the use of AI in the hiring process, both within the UK and the EU, through references to current and future legislative frameworks, cases and case studies. [ABSTRACT FROM AUTHOR]

Background: Surgical waiting lists have risen dramatically across the UK as a result of the COVID-19 pandemic. The effective use of operating theatres by optimal scheduling could help mitigate this, but this requires accurate case duration predictions. Current standards for predicting the duration of surgery are inaccurate. Artificial intelligence (AI) offers the potential for greater accuracy in predicting surgical case duration. This study aimed to investigate whether there is evidence to support that AI is more accurate than current industry standards at predicting surgical case duration, with a secondary aim of analysing whether the implementation of the models used produced efficiency savings. Method: PubMed, Embase, and MEDLINE libraries were searched through to July 2023 to identify appropriate articles. PRISMA extension for scoping reviews and the Arksey and O'Malley framework were followed. Study quality was assessed using a modified version of the reporting guidelines for surgical AI papers by Farrow et al. Algorithm performance was reported using evaluation metrics. Results: The search identified 2593 articles: 14 were suitable for inclusion and 13 reported on the accuracy of AI algorithms against industry standards, with seven demonstrating a statistically significant improvement in prediction accuracy (P < 0.05). The larger studies demonstrated the superiority of neural networks over other machine learning techniques. Efficiency savings were identified in a RCT. Significant methodological limitations were identified across most studies. Conclusion: The studies suggest that machine learning and deep learning models are more accurate at predicting the duration of surgery; however, further research is required to determine the best way to implement this technology. Waiting lists for elective surgeries are rising in the UK. We conducted a scoping review with the aim to understand if artificial intelligence has the potential to improve the utilization of operating theatre resources through improved scheduling. [ABSTRACT FROM AUTHOR]

Purpose: There is limited scholarship on artificial intelligence (AI) in higher education governance, despite the growing prevalence of AI-powered technologies in many fields, including education. However, as the technology is still nascent and has yet to reach its full potential, ideas and arguments abound, championing or cautioning against the use of these technologies. Design/Approach/Methods: To fill this gap in research on policy networks and AI in British higher education, this article employs network ethnography and discourse analysis to study how ideas about AI-powered technologies in higher education circulate in policy networks in the United Kingdom. Findings: The findings evidence a policy network showing signs of a heterarchy permeated by neoliberal rationales and populated by policy actors actively promoting artificial intelligence technologies to be used in education. Originality/Value: This paper builds on existing research by looking at the university and not-for-profit sectors, in addition to the governmental and educational technology sectors. Using network ethnography, this article expands our understanding of the policy actors involved and critically analyzes ideas regarding the use of AI in education. [ABSTRACT FROM AUTHOR]

This article discusses the importance of integrating cyber resilience into energy projects in the UK's critical national infrastructure. It highlights the growing threat of cyber-attacks and emphasizes the need for awareness and preparedness. The article also mentions international perceptions of cyber-attacks and the interconnectedness of critical infrastructure. It concludes by emphasizing the importance of cyber security and the need for increased resources to tackle the threat. The text also discusses the accountability for compliance with regulations and provides guidance on actions that must be taken. Overall, the document emphasizes the significance of cyber security in protecting critical infrastructure. This document is a glossary of terms related to cyber resilience and cybersecurity. It provides definitions for various terms to help readers understand the complex terminology used in the field. The text discusses quantum technology and its potential applications in various fields. It also mentions ransomware and provides information about secure design and vulnerability reporting services. The author of the text is Dr. Robert J. Chapman, an international risk management specialist. [Extracted from the article]

Many governments find it challenging to set up a regulatory regime to govern rapidly developing Autonomous Vehicles (AVs) technologies empowered by Artificial Intelligence. This paper analyzes flexible regulation as a tool for assessing regulatory reforms that govern disruptive innovations such as AVs. After defining flexible regulation as regulation that gives regulated entities choices for how to comply with the regulatory objectives, this paper develops Regulatory Flexibility Indicators (RFI) for rule structure, enforcement structure, and regulatory feedback. We study AVs regulatory reforms that took place recently in the United Kingdom and South Korea, focusing on how such reforms have enhanced regulation flexibility. This paper finds that regulatory governance in the United Kingdom is more flexible than in South Korea, indicating aspects of further reforms for improving regulatory flexibility. [ABSTRACT FROM AUTHOR]

Law is a global phenomenon and one of the highest revenue-generating industries. Due to its slow pace of development, it becomes difficult to adopt new technologies and tools for the better administration of the law. In the legal profession, researching is a requisite skill for lawyers. Even though legal research skills vary from lawyer to lawyer, even in the same case, every lawyer must engage in legal research to solve legal problems. Artificial Intelligence (AI) is a computer system that does tasks effectively and efficiently without any need for human intelligence. Much has been propounded on (AI) and the law in recent times, this paper focuses on elucidating AI and its relation to the practice and administration of the law, by addressing key issues on these topics. The paper aims to showcase that although the law is rigid, there is a very real possibility that it might change shortly with the help of artificial intelligence interference, which would change the working of the law in the country. It demonstrates the effects of artificial intelligence, both advantages and disadvantages and examines how they affect other areas of the law while using doctrinal methods of research. In India, there is no specific legislation governing artificial intelligence; the authors have looked into the laws of the US, and UK governing AI and have examined the Personal Data Protection Bill, 2019, and stated challenges that will be faced with the takeover of artificial intelligence. [ABSTRACT FROM AUTHOR]

Across the globe, governments are developing policies and strategies to reduce carbon emissions to address climate change. Monitoring the impact of governments' carbon reduction policies can significantly enhance our ability to combat climate change and meet emissions reduction targets. One promising area in this regard is the role of artificial intelligence (AI) in carbon reduction policy and strategy monitoring. While researchers have explored applications of AI on data from various sources, including sensors, satellites, and social media, to identify areas for carbon emissions reduction, AI applications in tracking the effect of governments' carbon reduction plans have been limited. This study presents an AI framework based on long short-term memory (LSTM) and statistical process control (SPC) for the monitoring of variations in carbon emissions, using UK annual CO2 emission (per capita) data, covering a period between 1750 and 2021. This paper used LSTM to develop a surrogate model for the UK's carbon emissions characteristics and behaviours. As observed in our experiments, LSTM has better predictive abilities than ARIMA, Exponential Smoothing and feedforward artificial neural networks (ANN) in predicting CO2 emissions on a yearly prediction horizon. Using the deviation of the recorded emission data from the surrogate process, the variations and trends in these behaviours are then analysed using SPC, specifically Shewhart individual/moving range control charts. The result shows several assignable variations between the mid-1990s and 2021, which correlate with some notable UK government commitments to lower carbon emissions within this period. The framework presented in this paper can help identify periods of significant deviations from a country's normal CO2 emissions, which can potentially result from the government's carbon reduction policies or activities that can alter the amount of CO2 emissions. [ABSTRACT FROM AUTHOR]

Autonomous vehicles ('AVs') promise to revolutionise transportation. Such vehicles use advanced sensors and artificial intelligence to navigate roads and avoid obstacles in traffic with limited to no human intervention. As AVs become more widely used, a critical question arises concerning who should be held liable for the harm their use might cause. This article shall examine if the Automated and Electric Vehicles Act 2018, product liability laws, and the law of contract can appropriately identify on whom liability falls where AVs cause accidents. Additionally, this article shall explore the trolley problem which confronts liability for AV-caused accidents: faced with inevitable harm to one or others, an AV must promptly elect to whom amongst different persons it should direct that harm, raising the question of who must be liable when an AV so acts. This paper aims to demonstrate that, although the current legal framework allows for the deployment of AVs, it requires significant adaptation to speak to the full prism of issues concerning AV-caused accident liability. [ABSTRACT FROM AUTHOR]

Simple Summary: According to cancer research statistics, there are 7500 new ovarian cancer diagnoses in the UK each year. An earlier detection of ovarian cancer leads to a better prognosis; however, there is currently no screening programme for ovarian cancer, and detection using ultrasound examinations remains challenging. The use of artificial intelligence in gynaecological ultrasound examinations aims to improve the diagnostic accuracy of ultrasound for ovarian cancer and improve outcomes for patients. This review aims to collate current research on AI in the ultrasound diagnosis of ovarian cancer and suggests the usefulness of incorporating this into clinical care. Ovarian cancer is the sixth most common malignancy, with a 35% survival rate across all stages at 10 years. Ultrasound is widely used for ovarian tumour diagnosis, and accurate pre-operative diagnosis is essential for appropriate patient management. Artificial intelligence is an emerging field within gynaecology and has been shown to aid in the ultrasound diagnosis of ovarian cancers. For this study, Embase and MEDLINE databases were searched, and all original clinical studies that used artificial intelligence in ultrasound examinations for the diagnosis of ovarian malignancies were screened. Studies using histopathological findings as the standard were included. The diagnostic performance of each study was analysed, and all the diagnostic performances were pooled and assessed. The initial search identified 3726 papers, of which 63 were suitable for abstract screening. Fourteen studies that used artificial intelligence in ultrasound diagnoses of ovarian malignancies and had histopathological findings as a standard were included in the final analysis, each of which had different sample sizes and used different methods; these studies examined a combined total of 15,358 ultrasound images. The overall sensitivity was 81% (95% CI, 0.80–0.82), and specificity was 92% (95% CI, 0.92–0.93), indicating that artificial intelligence demonstrates good performance in ultrasound diagnoses of ovarian cancer. Further prospective work is required to further validate AI for its use in clinical practice. [ABSTRACT FROM AUTHOR]

Digital technologies have the potential to transform all aspects of firms' operations. The emergence of advanced digital technologies such as Artificial Intelligence and Machine Learning raises questions about whether and when micro-businesses should adopt these technologies. In this paper we focus on how firms' adoption decisions on Artificial Intelligence and Machine Learning influence their innovation capabilities. Using survey data for over 6,000 micro-businesses in the UK, we identify two groups of adopters based on the timing of their adoption of Artificial Intelligence and Machine Learning. 'first movers' – early adopters of the new technologies - and 'second movers'- later adopters of the new technology. Probit models are used to investigate the innovation benefits of first and second mover adoption strategies. Our results suggest strong and positive impacts of adopting Artificial Intelligence and Machine Learning on micro-businesses' innovation outcomes and innovation processes. We highlight the differential benefits of first mover and second mover strategies and highlight the role of technology characteristics as the differentiating factor. Our results emphasize both the innovation enabling role of digital technologies and the importance of an appropriate strategic approach to adopting advanced digital technologies. Plain English Summary: Despite the powerful functions offered by advanced digital technologies, such as Artificial Intelligence and Machine Learning, it is unclear whether micro-businesses should adopt these technologies. In addition, micro-businesses are faced with two adoption strategy options: a first mover strategy by becoming an early adopter, or a second mover strategy by becoming a later adopter of the new technologies. Our study suggests that adopting Artificial Intelligence and Machine Learning enhances micro-businesses' innovation outcomes and innovation processes, highlighting the benefits of technology adoption on micro-businesses with limited financial and human resources. Interestingly, our study suggests the differential benefits of first mover and second mover strategies based on technology characteristics. The principal implication of this study is that micro-businesses should be encouraged to adopt Artificial Intelligence and Machine Learning to compensate for their resources and capabilities in the innovation process. [ABSTRACT FROM AUTHOR]

Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder affecting a large percentage of the adult population. A series of ongoing efforts has led to the development of a hybrid AI algorithm (a combination of a machine learningmodel and a knowledge-basedmodel) for assisting adult ADHDdiagnosis, and its clinical trial currently operating in the largest National Health Service (NHS) for adults with ADHD in the UK. Most recently, more data was made available that has lead to a total collection of 501 anonymized records as of 2022 July. This prompted the ongoing research to carefully examine the model by retraining and optimizing the machine learning algorithm in order to update the model with better generalization capability. Based on the large data collection so far, this paper also pilots a study to examine the effectiveness of variables other than the Diagnostic Interview for ADHD in adults (DIVA) assessment, which adds considerable cost in the screenining process as it relies on specially trained senior clinicians. Results reported in this paper demonstrate that the newly trained machine learning model reaches an accuracy of 75.03% when all features are used; the hybrid model obtains an accuracy of 93.61%. Exceeding what clinical experts expected in the absence of DIVA, achieving an accuracy of 65.27% using a rule-based machine learning model alone encourages the development of a cost effective model in the future. [ABSTRACT FROM AUTHOR]

This paper presents the results of a pilot interview study investigating the leisure reading habits of 20 practising AI researchers based in the United Kingdom. The interview analysis yields six areas in which literature plays a role in the field of AI: research focus, career choice, community formation, science communication, ethical thinking, and modelling of sociotechnical futures. These categories are proposed as the basis of a systematic taxonomy of the role of literature in AI research, evidencing literature's significance in AI laboratory and professional cultures. The paper presents the results of this preliminary investigation in combination with a synthesis of existing evidence in each category of influence. The aim of this hybrid approach is to cohere research and evidence in this relatively new area of study, and to present new findings contextually, in order to provide the foundations for further qualitative and quantitative research. [ABSTRACT FROM AUTHOR]

Training automated trading agents is a long-standing topic that has been widely discussed in artificial intelligence for the quantitative finance. Reinforcement learning (RL) is designed to solve the sequential decision-making tasks, like the stock trading. The output of the RL is the policy which can be presented as the probability values of the possible actions based on a given state. The policy is optimized by the reward function. However, even if the profit is considered as the natural reward function, a trading agent equipped with an RL model has several serious problems. Specifically, profit is only obtained after executing sell action, different profits exist at the same time step due to the varying-length transactions and the hold action deals with two opposite states, empty or nonempty position. To alleviate these shortcomings, in this paper, we introduce a new trading action called wait for the empty position status and design the appropriate rewards to all actions. Based on the new action space and reward functions, a novel approach named Transaction-aware Inverse Reinforcement Learning (TAIRL) is proposed. TAIRL rewards all trading actions for avoiding the reward bias and dilemma. TAIRL is evaluated by backtesting on 12 stocks of US, UK and China stock markets, and compared against other state-of-art RL methods and moving average trading methods. The experimental results show that the agent of TAIRL achieves the state-of-art performance in profitability and anti-risk ability. [ABSTRACT FROM AUTHOR]

Background: Allocation of scarce organs for transplantation is ethically challenging. Artificial intelligence (AI) has been proposed to assist in liver allocation, however the ethics of this remains unexplored and the view of the public unknown. The aim of this paper was to assess public attitudes on whether AI should be used in liver allocation and how it should be implemented. Methods: We first introduce some potential ethical issues concerning AI in liver allocation, before analysing a pilot survey including online responses from 172 UK laypeople, recruited through Prolific Academic. Findings: Most participants found AI in liver allocation acceptable (69.2%) and would not be less likely to donate their organs if AI was used in allocation (72.7%). Respondents thought AI was more likely to be consistent and less biased compared to humans, although were concerned about the "dehumanisation of healthcare" and whether AI could consider important nuances in allocation decisions. Participants valued accuracy, impartiality, and consistency in a decision-maker, more than interpretability and empathy. Respondents were split on whether AI should be trained on previous decisions or programmed with specific objectives. Whether allocation decisions were made by transplant committee or AI, participants valued consideration of urgency, survival likelihood, life years gained, age, future medication compliance, quality of life, future alcohol use and past alcohol use. On the other hand, the majority thought the following factors were not relevant to prioritisation: past crime, future crime, future societal contribution, social disadvantage, and gender. Conclusions: There are good reasons to use AI in liver allocation, and our sample of participants appeared to support its use. If confirmed, this support would give democratic legitimacy to the use of AI in this context and reduce the risk that donation rates could be affected negatively. Our findings on specific ethical concerns also identify potential expectations and reservations laypeople have regarding AI in this area, which can inform how AI in liver allocation could be best implemented. [ABSTRACT FROM AUTHOR]

In capital markets, digitisation and cloud technology have catalysed opportunities for AI by enabling firms to collect, store and analyse larger datasets from a variety of internal sources than ever before. The past year brought greater regulatory activity around AI in the UK, EU and US than in any prior year, although regulatory regimes remain disparate. When considering legal issues that arise from use of AI, facets of multiple legal fields are relevant, albeit differing between jurisdictions, such as intellectual property rights frameworks and contractual areas such as data ownership, data rights, risk and liability allocation and compliance responsibilities. In this paper, the key legal considerations and recent regulatory developments are categorised into four actionable, international themes: (i) due diligence of data as an asset; (ii) effective organisational ongoing controls; (iii) intellectual property management; and (iv) third party contract and risk management. With so much in flux, we find that the best approach for now is to consider such themes and ensure robust governance, transparency and explainability of each of the AI systems, human oversight of the AI and contractual arrangements with third party providers. Perhaps ironically, 'getting AI right' is currently, to a large extent, more an art of human judgement than any formulaic compliance with rules. [ABSTRACT FROM AUTHOR]

Right from the beginning, attendance has played an important role in the education systems, not only in student success but in the overall interest of the matter. Although all schools try to accentuate good attendance, still some schools find it hard to achieve the required level (96% in UK) of average attendance. The most productive way of increasing the pupils′ attendance rate is to predict when it is going to go down, understand the reasons—why it happened—and act on the affecting factors so as to prevent it. Artificial intelligence (AI) is an automated machine learning solution for different types of problems. Several machine learning (ML) models like logistic regression, decision trees, etc. are easy to understand; however, complicated (Neural Network, BART etc.) ML models are not transparent but are black-boxes for humans. It is not always evident how machine intelligence arrived at a decision. However, not always, but in critical applications it is important that humans can understand the reasons for such decisions. In this paper, we present a methodology on the application example of pupil attendance for constructing explanations for AI classification algorithms. The methodology includes building a model of the application in the Isabelle Insider and Infrastructure framework (IIIf) and an algorithm (PCR) that helps us to obtain a detailed logical rule to specify the performance of the black-box algorithm, hence allowing us to explain it. The explanation is provided within the logical model of the IIIf, thus is suitable for human audiences. It has been shown that the RR-cycle of IIIf can be adapted to provide a method for iteratively extracting an explanation by interleaving attack tree analysis with precondition refinement, which finally yields a general rule that describes the decision taken by a black-box algorithm produced by Artificial intelligence. [ABSTRACT FROM AUTHOR]

The National COVID-19 Chest Imaging Database (NCCID) is a centralized UK database of thoracic imaging and corresponding clinical data. It is made available by the National Health Service Artificial Intelligence (NHS AI) Lab to support the development of machine learning tools focused on Coronavirus Disease 2019 (COVID-19). A bespoke cleaning pipeline for NCCID, developed by the NHSx, was introduced in 2021. We present an extension to the original cleaning pipeline for the clinical data of the database. It has been adjusted to correct additional systematic inconsistencies in the raw data such as patient sex, oxygen levels and date values. The most important changes will be discussed in this paper, whilst the code and further explanations are made publicly available on GitLab. The suggested cleaning will allow global users to work with more consistent data for the development of machine learning tools without being an expert. In addition, it highlights some of the challenges when working with clinical multi-center data and includes recommendations for similar future initiatives. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI)-based healthcare applications (apps) are rapidly evolving, and radiology is a target specialty for their implementation. In this paper, we put the case for a national deployment registry to track the spread of AI apps into clinical use in radiology in the UK. By gathering data on the specific locations, purposes, and people associated with AI app deployment, such a registry would provide greater transparency on their spread in the radiology field. In combination with other regulatory and audit mechanisms, it would provide radiologists and patients with greater confidence and trust in AI apps. At the same time, coordination of this information would reduce costs for the National Health Service (NHS) by preventing duplication of piloting activities. This commentary discusses the need for a UK-wide registry for such apps, its benefits and risks, and critical success factors for its establishment. We conclude by noting that a critical window of opportunity has opened up for the development of a deployment registry, before the current pattern of localised clusters of activity turns into the widespread proliferation of AI apps across clinical practice., (Copyright © 2022 The Royal College of Radiologists. All rights reserved.)