Landscape Of Artificial Intelligence Research Articles

1179 Navigating the AI Landscape: Surveying the Use of AI Language Tools for Medical Portfolios

Abstract Aim Recent advances in artificial intelligence (AI) driven natural language processing have made it possible to use tools such as ChatGPT to generate pieces of text which are realistic, eloquent, and challenging to distinguish from human-generated content. This work aims to answer the question: could the use of these tools for medical portfolios represent a positive opportunity to enhance learning, or would it detract from the purpose of reflective practice? Method In total, 98 post-graduate doctors from across the UK were surveyed to gather data on their awareness of, previous use of, disclosure of use and experience with these tools for their professional portfolios. Their opinions on the need for future guidance in this area were also explored. Results From 90 respondents who currently maintain a professional portfolio, 18 (20%) had used AI-driven tools in some form to generate content for it and none disclosed this use. Regarding disclosing the use of AI-driven tools, 67.3% advocate for this whereas 32.7% believe disclosure is unnecessary. There was slightly stronger consensus regarding how useful guidance from regulatory bodies on this practice would be, with 73.5% in support of guidance and 26.5% in opposition. Conclusions Clearly, there is an urgent need for larger studies, discussion, and consensus from regulatory bodies so that guidance can be put in place. Without this, maintaining the integrity of CPD will become challenging for both trainee doctors and assessors of medical portfolios.

Frameworks for procurement, integration, monitoring, and evaluation of artificial intelligence tools in clinical settings: A systematic review.

Research on the applications of artificial intelligence (AI) tools in medicine has increased exponentially over the last few years but its implementation in clinical practice has not seen a commensurate increase with a lack of consensus on implementing and maintaining such tools. This systematic review aims to summarize frameworks focusing on procuring, implementing, monitoring, and evaluating AI tools in clinical practice. A comprehensive literature search, following PRSIMA guidelines was performed on MEDLINE, Wiley Cochrane, Scopus, and EBSCO databases, to identify and include articles recommending practices, frameworks or guidelines for AI procurement, integration, monitoring, and evaluation. From the included articles, data regarding study aim, use of a framework, rationale of the framework, details regarding AI implementation involving procurement, integration, monitoring, and evaluation were extracted. The extracted details were then mapped on to the Donabedian Plan, Do, Study, Act cycle domains. The search yielded 17,537 unique articles, out of which 47 were evaluated for inclusion based on their full texts and 25 articles were included in the review. Common themes extracted included transparency, feasibility of operation within existing workflows, integrating into existing workflows, validation of the tool using predefined performance indicators and improving the algorithm and/or adjusting the tool to improve performance. Among the four domains (Plan, Do, Study, Act) the most common domain was Plan (84%, n = 21), followed by Study (60%, n = 15), Do (52%, n = 13), & Act (24%, n = 6). Among 172 authors, only 1 (0.6%) was from a low-income country (LIC) and 2 (1.2%) were from lower-middle-income countries (LMICs). Healthcare professionals cite the implementation of AI tools within clinical settings as challenging owing to low levels of evidence focusing on integration in the Do and Act domains. The current healthcare AI landscape calls for increased data sharing and knowledge translation to facilitate common goals and reap maximum clinical benefit.

Navigating the decision-making landscape of AI in risk finance: Techno-accountability unveiled.

The integration of artificial intelligence (AI) systems has ushered in a profound transformation. This conversion is marked by revolutionary extrapolative capabilities, a shift toward data-centric decision-making processes, and the enhancement of tools for managing risks. However, the adoption of these AI innovations has sparked controversy due to their unpredictable and opaque disposition. This study employs the transactional stress model to empirically investigate how six technological stressors (techno-stressors) impact both techno-eustress (positive stress) and techno-distress (negative stress) experienced by finance professionals and experts. To collect data for this research, an e-survey was distributed to a diverse group of 251 participants from various sources. The findings, particularly the identification and development of techno-accountability as a significant factor, contribute to the risk analysis domain by improving the failure mode and effect analysis framework to better fit the rapidly evolving landscape of AI-driven innovations.

The advancement of artificial intelligence in biomedical research and health innovation: challenges and opportunities in emerging economies

The advancement of artificial intelligence (AI), algorithm optimization and high-throughput experiments has enabled scientists to accelerate the discovery of new chemicals and materials with unprecedented efficiency, resilience and precision. Over the recent years, the so-called autonomous experimentation (AE) systems are featured as key AI innovation to enhance and accelerate research and development (R&D). Also known as self-driving laboratories or materials acceleration platforms, AE systems are digital platforms capable of running a large number of experiments autonomously. Those systems are rapidly impacting biomedical research and clinical innovation, in areas such as drug discovery, nanomedicine, precision oncology, and others. As it is expected that AE will impact healthcare innovation from local to global levels, its implications for science and technology in emerging economies should be examined. By examining the increasing relevance of AE in contemporary R&D activities, this article aims to explore the advancement of artificial intelligence in biomedical research and health innovation, highlighting its implications, challenges and opportunities in emerging economies. AE presents an opportunity for stakeholders from emerging economies to co-produce the global knowledge landscape of AI in health. However, asymmetries in R&D capabilities should be acknowledged since emerging economies suffers from inadequacies and discontinuities in resources and funding. The establishment of decentralized AE infrastructures could support stakeholders to overcome local restrictions and opens venues for more culturally diverse, equitable, and trustworthy development of AI in health-related R&D through meaningful partnerships and engagement. Collaborations with innovators from emerging economies could facilitate anticipation of fiscal pressures in science and technology policies, obsolescence of knowledge infrastructures, ethical and regulatory policy lag, and other issues present in the Global South. Also, improving cultural and geographical representativeness of AE contributes to foster the diffusion and acceptance of AI in health-related R&D worldwide. Institutional preparedness is critical and could enable stakeholders to navigate opportunities of AI in biomedical research and health innovation in the coming years.

IMPACT OF AI ON FUTURE OF CONSUMER BEHAVIOUR

Artificial intelligence (AI) has become a revolutionary technology in recent years, changing many facets of our life. AI has significantly changed how we shop, from intelligent chatbots to personalised suggestions. This article examines how artificial intelligence (AI) affects human behaviour, including how it may improve shopping experiences and how it affects how we make decisions.Artificial intelligence (AI) is having a more and bigger impact on our daily lives and society. AI has a big influence on consumer behaviour, much like a lot of other powerful technology. Artificial Intelligence (AI) is revolutionising the way businesses engage with their clients. It influences their purchase decisions and shapes their behaviour in the process. The increasing impact of AI is not only altering business practices but also changing the ethical landscape of AI and encouraging customers to consider the moral implications of corporate decisions more carefully. Marketing professionals always need to know what customers want and need—ideally, before they even realise it. That task may be considerably simplified by artificial intelligence (AI), particularly with the development of deep learning. AI and technology are evolving, and so too must marketers and the tactics they use. Processes are already being automated to let marketers act more quickly and wisely.

Transforming academic librarianship through AI reskilling: Insights from the GPT-4 exploration program

The emergence of Artificial Intelligence (AI) in academic librarianship necessitates a corresponding evolution in librarians' competencies. This case study examines the GPT-4 Exploration Program at the University of New Mexico's College of University Libraries and Learning Sciences, which aimed to foster a culture of continuous learning and innovation by providing hands-on experience with advanced AI technology. Drawing on Malcolm Knowles' Adult Learning Principles, the program's structure and implementation are analyzed, highlighting the diverse range of projects undertaken by participants. The study reveals that effective AI reskilling involves cultivating a culture of continuous learning, adaptability, and collaborative exploration, anchored in a practical, hands-on approach. Participants reported significant improvements in AI literacy and confidence in applying AI tools to their work. The program's success demonstrates the potential of well-structured reskilling initiatives in empowering library professionals to navigate the evolving landscape of AI in academic libraries. The case study offers insights and strategies for other academic libraries seeking to develop AI reskilling initiatives, emphasizing the importance of fostering a supportive learning environment, addressing technical and ethical challenges, and ensuring the long-term sustainability of such programs. The GPT-4 Exploration Program serves as a blueprint for integrating AI into library services and operations.

Policy recommendations for integrating artificial intelligence into global trade agreements

The integration of artificial intelligence (AI) into global trade agreements presents a transformative opportunity to enhance efficiency, competitiveness, and innovation in international commerce. This review outlines policy recommendations aimed at facilitating the seamless incorporation of AI technologies into the framework of global trade agreements. As AI technologies continue to proliferate across various sectors, including manufacturing, logistics, and services, understanding the implications and challenges of their integration into trade agreements becomes paramount. This review provides insights into the current landscape of AI in global trade, highlighting both the existing challenges and opportunities for leveraging AI to drive economic growth and sustainable development. Key policy considerations include the harmonization of AI standards, addressing intellectual property rights and data ownership, facilitating cross-border data flows, ensuring transparency and accountability in AI decision-making, mitigating potential job displacement and inequality, and promoting ethical AI practices. Drawing on case studies and best practices from regional trade agreements and industry-specific implementations, this review offers actionable recommendations for policymakers, businesses, and international organizations. These recommendations emphasize the importance of collaborative efforts, capacity building, and monitoring mechanisms to effectively harness the benefits of AI while mitigating potential risks. By embracing AI as a driver of innovation and efficiency in global trade, stakeholders can foster a more inclusive and resilient trading environment that maximizes the benefits of technological advancements for all participants.

Choosing the right artificial intelligence solutions for your radiology department: key factors to consider.

The rapid evolution of artificial intelligence (AI), particularly in deep learning, has significantly impacted radiology, introducing an array of AI solutions for interpretative tasks. This paper provides radiology departments with a practical guide for selecting and integrating AI solutions, focusing on interpretative tasks that require the active involvement of radiologists. Our approach is not to list available applications or review scientific evidence, as this information is readily available in previous studies; instead, we concentrate on the essential factors radiology departments must consider when choosing AI solutions. These factors include clinical relevance, performance and validation, implementation and integration, clinical usability, costs and return on investment, and regulations, security, and privacy. We illustrate each factor with hypothetical scenarios to provide a clearer understanding and practical relevance. Through our experience and literature review, we provide insights and a practical roadmap for radiologists to navigate the complex landscape of AI in radiology. We aim to assist in making informed decisions that enhance diagnostic precision, improve patient outcomes, and streamline workflows, thus contributing to the advancement of radiological practices and patient care.

Developing effective prompts to improve communication with ChatGPT: a formula for higher education stakeholders

The escalating integration of artificial intelligence (AI) technologies, particularly the widespread use of ChatGPT in higher education, necessitates a profound exploration of effective communication strategies. This paper addresses the critical role of prompt development as a skill essential for university instructors engaging with ChatGPT. While emphasizing the practical implications for higher education, the study introduces a novel two-layered AI prompt formula, considering both components and elements. In methodology, the research synthesizes insights from existing models and proposes a tailored approach for ChatGPT, addressing its unique characteristics and the contextual elements within higher education. The results highlight the formula’s flexibility and potential applications in diverse fields, from syllabus planning to assessment. Moreover, the study identifies limitations inherent in ChatGPT, emphasizing the need for instructors to exercise caution in its usage. In conclusion, the paper underscores the evolving landscape of AI in education, envisaging specialized versions of ChatGPT for academic settings and advocating for the proactive adoption of ethical frameworks in the use of AI in higher education. This study serves as a foundational contribution to the discourse on effective AI communication in educational settings.

Knowledge, attitude, and practice of artificial intelligence among medical students in Sudan: A cross-sectional study

Introduction: In this cross-sectional study, we explored the knowledge, attitudes, and practices related to artificial intelligence (AI) among medical students in Sudan. With AI increasingly impacting healthcare, understanding its integration into medical education is crucial. This study aimed to assess the current state of AI awareness, perceptions, and practical experiences among medical students in Sudan. We aimed to evaluate the extent of AI familiarity among Sudanese medical students by examining their attitudes toward its application in medicine. Additionally, this study seeks to identify the factors influencing knowledge levels and explore the practical implementation of AI in the medical field. Method: A web-based survey was distributed to medical students in Sudan via social media platforms and email during October 2023. The survey included questions on demographic information, knowledge of AI, attitudes toward its applications, and practical experiences. The descriptive statistics, chi-square tests, logistic regression, and correlations were analysed using SPSS version 26.0. Results: Out of the 762 participants, the majority exhibited a basic understanding of AI, but detailed knowledge of its applications was limited. Positive attitudes toward the importance of AI in diagnosis, radiology, and pathology were prevalent. However, practical application of these methods was infrequent, with only a minority of the participants having hands-on experience. Factors influencing knowledge included the lack of a formal curriculum and gender disparities. Conclusion: This study highlights the need for comprehensive AI education in medical training programs in Sudan. While participants displayed positive attitudes, there was a notable gap in practical experience. Addressing these gaps through targeted educational interventions is crucial for preparing future healthcare professionals to navigate the evolving landscape of AI in medicine. Recommendations: Policy efforts should focus on integrating AI education into the medical curriculum to ensure readiness for the technological advancements shaping the future of healthcare.

Artificial intelligence in the battle against COVID-19: A comprehensive review

The COVID-19 pandemic has precipitated a global crisis, affecting all facets of human life. The rapid spread of the virus necessitated urgent responses from the healthcare sector, with artificial intelligence (AI) taking center stage as a pivotal tool in this fight. This paper provides a comprehensive review of the multifaceted role of AI during the pandemic, spanning from early detection and diagnosis to treatment, management, and the development of vaccines. We delve into the ethical and societal implications of deploying AI in such critical scenarios, discussing data privacy, algorithmic bias, and accessibility. The paper also presents various case studies, highlighting country-specific implementations and the dichotomy of success stories and failures. Furthermore, we explore the future directions of AI in healthcare, emphasizing emerging technologies and policy recommendations that could shape post-pandemic health-care systems. The conclusion synthesizes these insights, reflecting on the lessons learned and the prospective landscape of AI in global health. This paper aims to serve as a cornerstone for policymakers, health-care providers, and AI researchers, guiding the responsible and effective integration of AI in future health-care strategies.

Revolutionizing Talent Acquisition in Indonesia's E-Commerce Industry: The Transformative Impact of AI and Machine Learning

This research explores the impact of Artificial Intelligence (AI) and Machine Learning (ML) on talent acquisition within Indonesia's growing e-commerce sector. While acknowledging the transformative potential of these technologies, the study uncovers significant challenges in their integration into existing talent acquisition processes. These challenges include issues related to data quality, model accuracy, and system adaptability. The study emphasizes the difficulty of acquiring talent with expertise in AI and ML, given the increasing demand for skilled workers in the e-commerce boom. It explores strategies employed by companies to address this talent gap, such as upskilling existing staff and seeking external expertise. The research provides a nuanced understanding of AI and ML applications in the Indonesian e-commerce landscape, highlighting both benefits and obstacles. The insights derived from the study aim to offer actionable guidance for e-commerce firms, HR professionals, and researchers navigating the evolving landscape of AI, ML, and talent acquisition in Indonesia's digital marketplace.

Exploring Gender Bias and Algorithm Transparency: Ethical Considerations of AI in HRM

Opportunities and challenges are introduced by the integration of Artificial Intelligence (AI) into Human Resource Management (HRM). The paragraph discusses the ethical implications of AI applications in HRM, focusing on gender bias and algorithm transparency. It explores how AI-driven decision-making in HRM perpetuates gender bias, the importance of transparent algorithms for trust and accountability, and the role of regulatory frameworks in safeguarding ethical standards. The paper aims to provide a comprehensive analysis of the ethical landscape of AI in HRM and offers policy recommendations to mitigate bias and enhance transparency.

Navigating and reviewing ethical dilemmas in AI development: Strategies for transparency, fairness, and accountability

As artificial intelligence (AI) continues to permeate various aspects of our lives, the ethical challenges associated with its development become increasingly apparent. This paper navigates and reviews the ethical dilemmas in AI development, focusing on strategies to promote transparency, fairness, and accountability. The rapid growth of AI technology has given rise to concerns related to bias, lack of transparency, and the need for clear accountability mechanisms. In this exploration, we delve into the intricate ethical landscape of AI, examining issues such as bias and fairness, lack of transparency, and the challenges associated with accountability. To address these concerns, we propose strategies for transparency, including the implementation of Explainable AI (XAI), advocating for open data sharing, and embracing ethical AI frameworks. Furthermore, we explore strategies to promote fairness in AI algorithms, emphasizing the importance of fairness metrics, diverse training data, and continuous monitoring for iterative improvement. Additionally, the paper delves into strategies to ensure accountability in AI development, considering regulatory measures, ethical AI governance, and the incorporation of human-in-the-loop approaches. To provide practical insights, case studies and real-world examples are analyzed to distill lessons learned and best practices. The paper concludes with a comprehensive overview of the proposed strategies, emphasizing the importance of balancing innovation with ethical responsibility in the evolving landscape of AI development. This work contributes to the ongoing discourse on AI ethics, offering a roadmap for navigating the challenges and fostering responsible AI development practices.

Navigating the Future: AI’s Revolutionary Drive in the Auto Industry

The dawn of the 21st century has witnessed the automotive industry at the threshold of a paradigm shift, propelled by the integration of Artificial Intelligence (AI). This comprehensive exploration delves into the multifaceted role of AI as a harbinger of change, reshaping the contours of automotive design, manufacturing, and user experience. The paper commences with a historical overview, charting the ascent of AI from conceptual frameworks to its pervasive application across the automotive sector. A thorough literature review synthesizes seminal research, underscoring the operational efficiencies achieved, the impetus for innovation, and the challenges encountered in data stewardship and ethical governance. AI’s imprint on the industry is indelible, enhancing operational efficiency in production, quality control, and assembly, while simultaneously serving as the crucible for the development of connected and autonomous vehicles. The narrative, however, is nuanced by the challenges that accompany this technological march forward. The discourse addresses the intricacies related to data availability, quality, and system integration, alongside the ethical quandaries and regulatory conundrums that accompany the increased autonomy of AI-driven vehicles. Peering into the future, the paper proffers informed predictions about the trajectory of AI within the automotive industry, positing its potential to redefine mobility. Recommendations for future research and development are articulated, emphasizing the need to bridge identified literature gaps, particularly in the ethical deployment of AI and the pursuit of sustainable practices. In summation, the paper reaffirms the pivotal role of AI in the automotive industry, not solely as a catalyst for technological advancement but as a beacon for safety, efficiency, and environmental stewardship. The prospective landscape of AI in automotive paints a portrait of an interconnected, intelligent transportation ecosystem that promises to redefine the human experience of mobility. The conclusion encapsulates the essence of the discourse, advocating for a responsible embrace of AI that aligns with societal values and enhances the collective well- being

Unveiling the powerhouses of AI: A comprehensive study of GPU, FPGA, and ASIC accelerators

In the ever-evolving realm of technology, Artificial Intelligence (AI) has ushered in a transformative era, reshaping our interactions with digital systems, and expanding the horizons of machine capabilities. At the core of this AI revolution are specialized hardware entities known as AI accelerators. These accelerators, including Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application-Specific Integrated Circuits (ASICs), play a pivotal role in advancing AI applications across diverse domains. This paper delves into these accelerators, offering an in-depth exploration of their unique attributes and application domains. GPUs, initially designed for graphics, have evolved into versatile tools, thanks to their parallel computing prowess and efficient memory utilization. FPGAs, with reconfigurability and low latency, prove valuable in aerospace and neural network implementations, though they come with cost and expertise challenges. ASICs, engineered for specific functions, excel in performance and power efficiency for mass production but require significant time and resources for development. Furthermore, this paper presents practical application analyses, showcasing how these accelerators are effectively deployed in real-world scenarios. With this comprehensive exploration, readers gain a deeper understanding of AI accelerators and their transformative impact on the AI landscape.

The Ethics of Artificial Intelligence: An Empirical Overlook

This research paper delves into the intricate web of ethical concerns surrounding the ever-evolving realm of Artificial Intelligence (AI). It embarks on a journey to elucidate the multifaceted landscape of AI, spanning its definition, historical evolution, and contemporary applications. The core of this investigation revolves around the pivotal ethical dimensions inherent to AI, encompassing topics such as bias mitigation, privacy preservation, accountability enforcement, transparency enhancement, explainability pursuit, alignment with human values, and the latent potential for AI to exacerbate existing societal inequalities. An extensive review of the existing literature on AI ethics, conducted within this paper, unveils a rich tapestry of insights, illuminating critical findings and charting promising avenues for future research. It maps the evolution of AI ethics discourse, offering an analytical prism to comprehend the evolving contours of this dynamic field. In its concluding remarks, this research paper extends its purview to the global stage, advocating the imperative need for the establishment of international standards governing the ethical deployment of AI. It is an earnest endeavor to synthesize the collective wisdom garnered through this exploration into actionable recommendations that can guide the responsible development and utilization of AI, thereby ensuring that its transformative power is harnessed in a manner that is both technologically profound and ethically impeccable. Keywords AI Ethics, Evolution of AI Ethics, Global Standards, Human Values Alignment

The Evolving Regulatory Paradigm of AI in MedTech: A Review of Perspectives and Where We Are Today.

Artificial intelligence (AI)-enabled technologies in the MedTech sector hold the promise to transform healthcare delivery by improving access, quality, and outcomes. As the regulatory contours of these technologies are being defined, there is a notable lack of literature on the key stakeholders such as the organizations and interest groups that have a significant input in shaping the regulatory framework. This article explores the perspectives and contributions of these stakeholders in shaping the regulatory paradigm of AI-enabled medical technologies. The formation of an AI regulatory framework requires the convergence of ethical, regulatory, technical, societal, and practical considerations. These multiple perspectives contribute to the various dimensions of an evolving regulatory paradigm. From the global governance guidelines set by the World Health Organization (WHO) to national regulations, the article sheds light not just on these multiple perspectives but also on their interconnectedness in shaping the regulatory landscape of AI.

From Analog to Algorithm

In the digital age, the current Management literature remains heavily rooted in anthropocentric and analog perspectives, often overlooking the profound influence of algorithms and artificial intelligence (AI) in modern organizational dynamics. To this end, this systematic literature review seeks to bridge this gap by exploring the evolving landscape of AI in organizations and the role of the Actor-Network Theory (ANT) as a theoretical-methodological lens for understanding AI’s impact on Management. Through a systematic literature review, we address research on the utilization of AI in Management over the past decade, the applications of ANT in non-IT or non-AI management studies, and studies explaining IT or AI concepts via ANT. Our findings highlight a significant research gap in understanding human-machine interactions and Business Management within organizations that use AI. Additionally, we identify potential avenues for future scholarly contributions, emphasizing the need for a more integrated approach that considers human and machine actors in organizational contexts.

Change Management Trends in the AI Modern World: Adapting to the Future of Work

Change management is a crucial factor in guiding organizations through the swiftly evolving realm of artificial intelligence (AI) and its impact on the contemporary workplace. With AI technology continuously progressing, organizations are compelled to adapt and employ effective change management tactics to maintain their competitiveness and resilience. This article delves into the prevailing trends in change management in the context of the AI modern world, shedding light on the difficulties and prospects for organizations as they embrace and integrate AI into the future of work. It examines the challenges and opportunities that accompany this transition, considering how organizations can implement robust change management strategies to successfully navigate the dynamic landscape of AI in the workplace. Keywords: Change Management, Artificial Intelligence, Modern World, Digital Transformation, Organizational Change, AI Impact On Work, Change Management Strategies Journal Reference Format: Ademola, O. E. (2024): Change Management Trends in the AI Modern World: Adapting to the Future of Work. Journal of Behavioural Informatics, Digital Humanities and Development Rese Vol. 10 No. 1. Pp 41-50. https://www.isteams.net/behavioralinformaticsjournal dx.doi.org/10.22624/AIMS/V10N1P3