Advances In Artificial Intelligence Research Articles

Social Influence, Trust and Future Usage: A study of BIPOC Users of CHATGPT and other AI Chatbots

ABSTRACTIn this paper we examine the determinants (trust and social influence) that influence users' future use intentions of generative artificial intelligence (AI) applications among black, indigenous and other people of color (BIPOC) users of generative AI applications such as ChatGPT and other large language models. An online survey instrument was administered to 119 BIPOC identified individuals residing in the United States. Descriptive and inferential statistics were used to analyze data through SPSS. Results indicate no statistically significant differences among ethnic/ racial groups in social influence and continued use intentions. However, with respect to trust, Hispanic/LatinX users perceive information from AI chatbots as more trustworthy and accurate, and that their search activity was more secure than other BIPOC populations. The study also found that trust and social influence were significant factors driving future use intentions.

Reliability of generative artificial intelligence in identifying the major risk factors for venous thrombosis

Reliability of generative artificial intelligence in identifying the major risk factors for venous thrombosis

S878 A Novel Web Application Utilizing Generative Artificial Intelligence to Enhance Endoscopy Education for Gastroenterology Fellows

S878 A Novel Web Application Utilizing Generative Artificial Intelligence to Enhance Endoscopy Education for Gastroenterology Fellows

MetaPath Chat: multimodal generative artificial intelligence chatbot for clinical pathology.

MetaPath Chat: multimodal generative artificial intelligence chatbot for clinical pathology.

Leveraging ChatGPT in the Writing Classrooms: Theoretical and Practical insights

This paper explores the transformative potential of integrating generative artificial intelligence (AI) technologies, particularly ChatGPT, into second language (L2) writing pedagogy. The paper begins by examining the evolution of L2 writing pedagogy and highlighting the strengths and limitations of each. It then discusses the integration of ChatGPT into eight stages of the writing process, such as preparation, modeling, planning, and collaborative writing, emphasizing the importance of ethical and responsible use of AI. Key challenges, including over-reliance on AI, ethical concerns, and privacy issues, are identified, with practical recommendations provided to address these concerns. Theoretical frameworks relevant to AI adoption are also discussed to offer insights into how L2 writing teachers and students might embrace AI tools. The paper concludes with research recommendations to ensure the effective and responsible integration of generative AI into L2 writing instruction.

Does the human professor or artificial intelligence (AI) offer better explanations to students? Evidence from three within-subject experiments

ABSTRACT Three within-subject experiments were conducted by providing students with answers to content questions across different subject matters (a definition, explanation, and example) offered by a human professor (subject-matter expert) versus generative artificial intelligence (ChatGPT). In a randomized order, students read both the expert and ChatGPT’s responses (both were de-identified and declared to be “professors,” so students were not aware one was artificial intelligence), rated both explanations on teaching clarity and competence, and then reported on their affect toward the content and situational interest. Study 1 (interpersonal communication content) revealed no significant differences in repeated measure ratings comparing the expert versus ChatGPT. However, in Study 2 (business communication content) and Study 3 (instructional communication content), compared with the expert, ChatGPT (impersonating a professor) was rated by the same students as higher in teaching clarity and competence, and it generated more student affect and situational interest. In Study 2 and Study 3, a within-subjects mediation analysis revealed that ChatGPT generated more student affect toward the content through the clarity in responses it provided to students.

Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environments

Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environments

Assessments of Generative Artificial Intelligence as Clinical Decision Support Ought to be Incorporated Into Randomized Controlled Trials of Electronic Alerts for Acute Kidney Injury

Assessments of Generative Artificial Intelligence as Clinical Decision Support Ought to be Incorporated Into Randomized Controlled Trials of Electronic Alerts for Acute Kidney Injury

APPLICATIONS OF MULTIMODAL GENERATIVE ARTIFICIAL INTELLIGENCE IN A REAL-WORLD RETINA CLINIC SETTING.

This study evaluates a large language model, Generative Pre-trained Transformer 4 with vision, for diagnosing vitreoretinal diseases in real-world ophthalmology settings. A retrospective cross-sectional study at Bascom Palmer Eye Clinic, analyzing patient data from January 2010 to March 2023, assesses Generative Pre-trained Transformer 4 with vision's performance on retinal image analysis and International Classification of Diseases 10th revision coding across 2 patient groups: simpler cases (Group A) and complex cases (Group B) requiring more in-depth analysis. Diagnostic accuracy was assessed through open-ended questions and multiple-choice questions independently verified by three retina specialists. In 256 eyes from 143 patients, Generative Pre-trained Transformer 4-V demonstrated a 13.7% accuracy for open-ended questions and 31.3% for multiple-choice questions, with International Classification of Diseases 10th revision code accuracies at 5.5% and 31.3%, respectively. Accurately diagnosed posterior vitreous detachment, nonexudative age-related macular degeneration, and retinal detachment. International Classification of Diseases 10th revision coding was most accurate for nonexudative age-related macular degeneration, central retinal vein occlusion, and macular hole in OEQs, and for posterior vitreous detachment, nonexudative age-related macular degeneration, and retinal detachment in multiple-choice questions. No significant difference in diagnostic or coding accuracy was found in Groups A and B. Generative Pre-trained Transformer 4 with vision has potential in clinical care and record keeping, particularly with standardized questions. Its effectiveness in open-ended scenarios is limited, indicating a significant limitation in providing complex medical advice.

Dietetic Students' Knowledge and Perceptions of Their Use of Generative Artificial Intelligence Now and in the Future

Dietetic Students' Knowledge and Perceptions of Their Use of Generative Artificial Intelligence Now and in the Future

Exploring opportunities for language immersion in the posthuman spectrum: lessons learned from digital agents

Purpose Technologically-enhanced language education has shifted from computer-assisted language learning (CALL) to mobile-assisted language learning (MALL), including the use of conversational digital agents, and more recently, towards the use of generative artificial intelligence (AI) large language model (LLM) programmes for language learning purposes. This paper aims to explore the interplay between such posthuman communication and posthumanist applied linguistics, and between digital agents and human agency in response to the increasing permeation of AI in life and learning. Design/methodology/approach A core team of four researchers investigated how digital agents could be leveraged to support immersive target language learning and practice, focusing specifically on the conversational AI that pervaded digitally-mediated communication prior to the release of generative AI. Each researcher engaged in a digital autoethnography using conversational agents found in the digital wilds to learn a target second language via digital immersion. Findings Through qualitative data analysis of autoethnographic narratives using NVIVO, four key thematic codes characterizing the learning journeys emerged: context, language learning, posthuman engagement and technological parameters. The posthuman learning experiences conflicted with the multisensory, embodied and embedded ethos of posthumanist applied linguistics, indicating that informed human pedagogical agency must crucially be exercised to benefit from the learning potential of posthuman agents. Interactions with conversational agents did provide small-scale, just-in-time learning opportunities, but these fell short of immersive learning. Originality/value The methodology and findings offer a unique and valuable lens on the language learning potential of emerging LLM-based generative agents that are rapidly infusing conversational practices.

ChatGPT: A canary in the coal mine or a parrot in the echo chamber? Detecting fraud with LLM: The case of FTX

Does the paradigm shift brought by Large Language Models (LLMs) hold the promise of revolutionizing financial analysis? Our article tackles this question by exploring fraud detection in cryptocurrency exchanges, with a focus on FTX. We study the abilities of generative artificial intelligence tools like ChatGPT to serve as early-warning systems of fraud and identify red flags in the particular and difficult case where no financial information is available. We recognize several challenges to provide insights beyond human knowledge. To achieve a higher degree of scrutiny, we highlight the role of sequential interactions between the AI Chatbot and the researcher as well as the inclusion of external contents, a technique known as Retrieval Augmented Generation (RAG). Therefore, this article serves as a cautionary tale on the necessary conditions to achieve augmented intelligence.

Feasibility of Using Two Generative AI Models for Teeth Reconstruction

ObjectivesThis feasibility study investigates the application of artificial intelligence (AI) models, specifically transformer-based (TM) and diffusion-based (DM) models, for the reconstruction of single and multiple missing teeth. MethodsA dataset of 129 digitized models was utilized to create both TM and DM models. Single and multiple missing teeth were artificially generated. Reconstruction accuracy was assessed against ground truth data using Root Mean Square (RMS) and mean absolute error (MAE) across various artificially generated teeth. Paired t-tests were used for analyzing differences between the two models (p<0.05). ResultsBoth TM and DM models demonstrated similar accuracy in the reconstruction of single and multiple missing teeth. The greatest disparity occurred in the reconstruction of all remaining teeth, with the exception of 33 and 43 for both models (RMS TM: 0.37; DM: 0.43). TM exhibited the highest precision in reconstructing tooth 34 (RMS: 0.21), whereas DM demonstrated superior accuracy in reconstructing tooth 21 (RMS: 0.19). Despite there was no significant difference between the models. ConclusionsAI-based TM and DM models demonstrate promising results in reconstructing missing teeth, with superior accuracy in single-tooth compared to multiple-tooth edentulous spaces. Despite the need for additional refining and larger datasets, including antagonistic teeth, these models have the potential to streamline and improve the dental restoration processes, potentially leading to cost savings and enhanced clinical outcomes. SignificanceThis study demonstrates the feasibility and potential of transformer- and diffusion-based AI models to accurately reconstruct missing teeth, offering a novel approach that could streamline and enhance the precision of implant planning.

Strengths, Weaknesses, Opportunities, and Threats Analysis of Integrating ChatGPT Into Nursing Education

Artificial intelligence (AI) is driving global change, and the implementation of generative AI in higher education is inevitable. AI language models such as the chat generative pre-trained transformer (ChatGPT) hold the potential to revolutionize the delivery of nursing education in the future. Nurse educators play a crucial role in preparing nursing students for a future technology-integrated healthcare system. While the technology has limitations and potential biases, the emergence of ChatGPT presents both opportunities and challenges. It is critical for faculty to be familiar with the capabilities and limitations of this model to foster effective, ethical, and responsible utilization of AI technology while preparing students in advance for the dynamic and rapidly advancing landscape of nursing and healthcare. Therefore, this article was written to present a strengths, weaknesses, opportunities, and threats (SWOT) analysis of integrating ChatGPT into nursing education, providing a guide for implementing ChatGPT in nursing education and offering a well-rounded assessment to help nurse educators make informed decisions.

Generative AI for Self-Adaptive Systems: State of the Art and Research Roadmap

Self-adaptive systems (SASs) are designed to handle changes and uncertainties through a feedback loop with four core functionalities: monitoring, analyzing, planning, and execution. Recently, generative artificial intelligence (GenAI), especially the area of large language models, has shown impressive performance in data comprehension and logical reasoning. These capabilities are highly aligned with the functionalities required in SASs, suggesting a strong potential to employ GenAI to enhance SASs. However, the specific benefits and challenges of employing GenAI in SASs remain unclear. Yet, providing a comprehensive understanding of these benefits and challenges is complex due to several reasons: limited publications in the SAS field, the technological and application diversity within SASs, and the rapid evolution of GenAI technologies. To that end, this article aims to provide researchers and practitioners a comprehensive snapshot that outlines the potential benefits and challenges of employing GenAI’s within SAS. Specifically, we gather, filter, and analyze literature from four distinct research fields and organize them into two main categories to potential benefits: (i) enhancements to the autonomy of SASs centered around the specific functions of the MAPE-K feedback loop, and (ii) improvements in the interaction between humans and SASs within human-on-the-loop settings. From our study, we outline a research roadmap that highlights the challenges of integrating GenAI into SASs. The roadmap starts with outlining key research challenges that need to be tackled to exploit the potential for applying GenAI in the field of SAS. The roadmap concludes with a practical reflection, elaborating on current shortcomings of GenAI and proposing possible mitigation strategies. †

Artificial General Intelligence (AGI): A Comprehensive Review

Artificial General Intelligence (AGI) represents a significant leap in the field of artificial intelligence, defined by its ability to perform any intellectual task that a human can. Unlike narrow AI, which is task-specific, AGI is characterized by versatility, adaptability, autonomy, and reasoning capabilities. This comprehensive review explores the defining features of AGI, including its cognitive flexibility and capacity for autonomous decision-making and problem-solving. The current state of AGI research is examined, highlighting key areas such as cognitive architectures, neuroscience-inspired AI, and machine learning and deep learning advancements. The review also addresses the technical challenges and ethical considerations associated with AGI development, including potential impacts on employment, privacy, and security, as well as the necessity for robust safety and control measures. Looking ahead, the potential benefits of AGI in various domains, such as healthcare, climate change, education, and economic growth, are discussed. Finally, the importance of establishing ethical frameworks and governance structures to guide AGI development and usage is emphasized. By addressing these challenges and fostering collaboration among scientists, policymakers, and society, AGI can be developed and deployed to benefit humanity as a whole.

Leveraging Generative AI for Personalized Healthcare: Revolutionizing Treatment Plans

This comprehensive article explores the transformative potential of generative artificial intelligence (AI) in healthcare, examining its applications in data analysis, disease outcome prediction, and personalized treatment planning. The article discusses how generative AI systems can process vast amounts of multi-modal patient data to uncover subtle patterns and correlations, leading to more accurate diagnoses and tailored treatment strategies. It highlights successful implementations across various medical specialties, including oncology, chronic disease management, and rare disease diagnosis. The article also addresses the challenges and ethical considerations associated with the deployment of generative AI in healthcare, emphasizing the need for data privacy, algorithmic transparency, and regulatory compliance. By presenting both the opportunities and obstacles, this work provides a balanced view of the role generative AI can play in ushering in a new era of precision medicine and patient-centered care

Generative AI, Innovation, and Trust

Our paper explores the integration of generative artificial intelligence (GenAI) into organizations’ innovation and new product development processes, focusing on when and how to trust AI-generated outcomes in this context. We propose a framework to assess the level of trust required based on task-specific needs and the distinction between general and expert AI models. While inaccuracies in GenAI outputs can foster creativity during ideation, higher accuracy, and trust are essential for tasks requiring domain-specific expertise. The paper concludes by discussing the necessary human capabilities and organizational strategies for effectively deploying GenAI in innovation management.

Gender Bias in Text-to-Image Generative Artificial Intelligence When Representing Cardiologists

Introduction: While the global medical graduate and student population is approximately 50% female, only 13–15% of cardiologists and 20–27% of training fellows in cardiology are female. The potentially transformative use of text-to-image generative artificial intelligence (AI) could improve promotions and professional perceptions. In particular, DALL-E 3 offers a useful tool for promotion and education, but it could reinforce gender and ethnicity biases. Method: Responding to pre-specified prompts, DALL-E 3 via GPT-4 generated a series of individual and group images of cardiologists. Overall, 44 images were produced, including 32 images that contained individual characters and 12 group images that contained between 7 and 17 characters. All images were independently analysed by three reviewers for the characters’ apparent genders, ages, and skin tones. Results: Among all images combined, 86% (N = 123) of cardiologists were depicted as male. A light skin tone was observed in 93% (N = 133) of cardiologists. The gender distribution was not statistically different from that of actual Australian workforce data (p = 0.7342), but this represents a DALL-E 3 gender bias and the under-representation of females in the cardiology workforce. Conclusions: Gender bias associated with text-to-image generative AI when using DALL-E 3 among cardiologists limits its usefulness for promotion and education in addressing the workforce gender disparities.

Harnessing the Power of Generative Artificial Intelligence in Pathology Education.

Generative artificial intelligence (AI) technologies are rapidly transforming numerous fields, including pathology, and hold significant potential to revolutionize educational approaches. To explore the application of generative AI, particularly large language models and multimodal tools, for enhancing pathology education. We describe their potential to create personalized learning experiences, streamline content development, expand access to educational resources, and support both learners and educators throughout the training and practice continuum. We draw on insights from existing literature on AI in education and the collective expertise of the coauthors within this rapidly evolving field. Case studies highlight practical applications of large language models, demonstrating both the potential benefits and unique challenges associated with implementing these technologies in pathology education. Generative AI presents a powerful tool kit for enriching pathology education, offering opportunities for greater engagement, accessibility, and personalization. Careful consideration of ethical implications, potential risks, and appropriate mitigation strategies is essential for the responsible and effective integration of these technologies. Future success lies in fostering collaborative development between AI experts and medical educators, prioritizing ongoing human oversight and transparency to ensure that generative AI augments, rather than supplants, the vital role of educators in pathology training and practice.