AI Language Research Articles

A cross-sectional study to assess response generated by ChatGPT and ChatSonic to patient queries about Epilepsy

ObjectiveThis article presents a study comparing the responses of two AI chatbots, ChatGPT and ChatSonic, regarding inquiries about epilepsy. Overall, ChatGPT and ChatSonic are very similar in terms of their capabilities and limitations and they are the most widely used AI software. However, there are some key differences, such as their training data, supported languages, and pricing model. The study aims to assess the potential application of AI in patient counseling and decision-making regarding epilepsy treatment. MethodsThe study categorized the inquiries of patients about epilepsy into two groups: patient counseling and judgment. Ten questions were formulated within these categories. Two specialized doctors evaluated the reliability and accuracy of the chatbot replies using the Global Quality Scale (GQS) and a modified version of the DISCERN score. ResultsThe median value for GQS of 4.5 was given by Evaluator JC, and a median value for GQS of 4.0 was given by Evaluator VV. Furthermore, a median for RS of 5.0 was given by Evaluator JC, and a median for RS of 4.0 was given by Evaluator VV. The GQS data from Evaluators JC and VV have a Spearman correlation coefficient of -0.531, indicating an inversely proportional association, and a p-value of 0.016, indicating a statistically significant relationship between the variables. However, the correlation coefficient of RS between data by Evaluator JC and Evaluator VV is 0.368 which indicates the correlation is a directly proportional relationship, with a p-value of 0.110 which is not statistically significant, does not establish a relation between the variables. Weighted Kappa was used to study the agreement between the data. With a weighted kappa value of -0.318 and a 95 %CI of -0.570, -0.065 was obtained for GQS. This can help reject the null hypothesis indicating that the values by the Evaluator JC and Evaluator VV are statistically significant and has a negative agreement. However, a weighted kappa value of 0.1327 with a 95 %CI of -0.093, 0.359 obtained for RS, fails to reject the null hypothesis indicating that the values by the Evaluator JC and Evaluator VV are not significant and no agreement exists between the Evaluators. The results of this study suggest that both ChatGPT and ChatSonic have the potential to be valuable tools for epilepsy patients and their healthcare providers. However, it is important to note that the two evaluators had better agreement on the GQS scores than on the RS scores, suggesting that the GQS may be a more reliable measure of the quality of chatbot responses. ConclusionThe findings underscore the importance of collaboration among policymakers, healthcare professionals, and AI designers to ensure appropriate and safe utilization of AI chatbots in the healthcare domain. While AI chatbots can provide valuable information, it is crucial to acknowledge their limitations, including reliance on the training data and occasional factual errors. The study highlights the need for further testing and validation of AI language models in the management of epilepsy as it concludes.

Assessing the Viability and Effectiveness of Chatgpt Applications in the Customer Service Industry: A Study on Business Models and User Experience

This study assesses the viability and effectiveness of integrating ChatGPT, an AI language model, in the customer service industry. Customer satisfaction and overall experience improvement are critical in defining the extent of business success. The study examines different trends and future research opportunities, showcasing the applicability of ChatGPT in hospitality, information technology, and global retailing industries. It also highlights the potential effectiveness of ChatGPT to revolutionize the sectors mentioned above and boost customer trust for sustainable development. Moreover, the research assesses the extent of ChatGPT usage in the organizational department by uncovering factors influencing its adoption using surveys. The research concludes that ChatGPT’s integration in customer service operations offers improved response time, cost-effectiveness, and scalability, allowing the business to reach a global audience. By embracing ChatGPT and its potential, organizations can develop seamless and customer-centric support experiences, gaining a competitive edge over other organizations, especially in the 21st dynamic market.

Artificial Intelligence in Scoliosis Classification: An Investigation of Language-Based Models.

Open-source artificial intelligence models are finding free application in various industries, including computer science and medicine. Their clinical potential, especially in assisting diagnosis and therapy, is the subject of increasingly intensive research. Due to the growing interest in AI for diagnostics, we conducted a study evaluating the abilities of AI models, including ChatGPT, Microsoft Bing, and Scholar AI, in classifying single-curve scoliosis based on radiological descriptions. Fifty-six posturographic images depicting single-curve scoliosis were selected and assessed by two independent neurosurgery specialists, who classified them as mild, moderate, or severe based on Cobb angles. Subsequently, descriptions were developed that accurately characterized the degree of spinal deformation, based on the measured values of Cobb angles. These descriptions were then provided to AI language models to assess their proficiency in diagnosing spinal pathologies. The artificial intelligence models conducted classification using the provided data. Our study also focused on identifying specific sources of information and criteria applied in their decision-making algorithms, aiming for a deeper understanding of the determinants influencing AI decision processes in scoliosis classification. The classification quality of the predictions was evaluated using performance evaluation metrics such as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and balanced accuracy. Our study strongly supported our hypothesis, showing that among four AI models, ChatGPT 4 and Scholar AI Premium excelled in classifying single-curve scoliosis with perfect sensitivity and specificity. These models demonstrated unmatched rater concordance and excellent performance metrics. In comparing real and AI-generated scoliosis classifications, they showed impeccable precision in all posturographic images, indicating total accuracy (1.0, MAE = 0.0) and remarkable inter-rater agreement, with a perfect Fleiss' Kappa score. This was consistent across scoliosis cases with a Cobb's angle range of 11-92 degrees. Despite high accuracy in classification, each model used an incorrect angular range for the mild stage of scoliosis. Our findings highlight the immense potential of AI in analyzing medical data sets. However, the diversity in competencies of AI models indicates the need for their further development to more effectively meet specific needs in clinical practice.

P22 Disrupting Health Economics: Automating Network Meta-Analyses With AI and Large Language Models

P22 Disrupting Health Economics: Automating Network Meta-Analyses With AI and Large Language Models

Customer journey optimisation using large language models: Best practices and pitfalls in generative AI

Today's business environment is moving faster than ever, and the expressive and adaptive capabilities of generative AI (GenAI) and large language models (LLMs) are redefining the enterprise rails of tomorrow. Given the abundance of industry hype, investor expectations and leadership pressure, the initial impulse is to ‘get in the game’. But how does one implement initiatives that drive business outcomes within ethical parameters while avoiding technical pitfalls? Marketers need practical guidance to navigate through these changes. In this paper, the authors examine multiple considerations for deployment of GenAI in marketing and customer experience. How does the marketer decide on which initiatives and opportunities to begin with? Which use cases will drive value as the organisation adapts to deploying these new capabilities? Once a marketer has identified the opportunities to capitalise on through GenAI, how is the capability deployed? There are a variety of approaches that can be considered given the level of organisational capability with AI and resource levels to be applied. As with any cutting-edge capability, there are potential missteps that must be avoided to ensure success. This paper provides some insight based on practical experiences to date that cover ethical, technical and process concerns. The paper presents thoughtful approaches to the deployment of LLMs and GenAI that can result in concrete ROI and reduced risk even in this early stage of adoption. With this information, marketers can be prepared to confidently begin their journey using GenAI to transform their customer experience and drive enterprise value for their organisations.

Development and evaluation of a software system for medical students to teach and practice anamnestic interviews with virtual patient avatars

Background and ObjectivesTaking a medical history is a core competence of the diagnostic process. At the beginning of their study medical students need to learn and practice the necessary techniques, initially focusing on good structuring and completeness. For this purpose, an interactive software system (ViPATalk) was developed in which the student can train to pose questions to virtual patient avatars in free conversation. At the end, the student receives feedback on the completeness of the questioning and an explanation of the essential items. The use of this software was compared to the traditional format of student role play in a randomized trial. MethodsThe central component of ViPATalk is a chatbot based on the AI language AIML, which generates an appropriate answer based on keywords in the student's question. To enable a realistic use, the student can enter the question via microphone (speech-to-text) and the answer generated by the chatbot is presented as a short video sequence, where the avatar is generated from a real image. Here, the transition between the sequences is seamless, resulting in a continuous movement of the avatar during the conversation. ResultsThe learning success by practicing with ViPATalk was tested in an anamnestic interview with actors as simulated patients. The completeness of the conversation was evaluated with regard to numerous aspects and also certain behaviors during the conversation. These results were compared with those after practicing using peer role play. ConclusionsIt was found that practicing with ViPATalk was mostly equivalent to the students' role play. In the subsequent survey of the students, the wish was expressed that the ViPATalk software should also be used as an online tool for self-study and that there should be more cases for practicing.

AI Content Generation Technology based on Open AI Language Model

The Open AI language model is a powerful tool for generating AI content. A Large amount of text data is trained through the language model, which can generate new text that is similar in style and tone to the training data. This language model can assist writers in generating high-quality content by offering suggestions and insights for improving language usage, sentence structure, and overall readability. This study represents the development of a content generation tool based on the open AI language model by utilising GPT 3 in the backend as an API to generate the necessary information for the model. With the help of this tool, businesses and individuals can produce high-quality, engaging content more efficiently than ever before. This content generation tool uses a recurrent neural network (RNN) architecture, which enables it to make more accurate predictions than rule-based chatbots. All the features, like Facebook ads, LinkedIn posts, Amazon product descriptions, blogs, company bios, chat bots, and so on, will be presented in the dashboard. This tool is powered by advanced machine learning algorithms that can analyse and understand natural language, allowing them to produce content that is grammatically correct, free of errors, and tailored to specific audiences. They can also help optimize content for search engines, ensuring that it reaches a wider audience and generates more traffic with fine-tuning templates.

수학교육의 교수학적 실천과 연구 측면에서 AI 언어 모델 활용 방안 탐색

Due to the recent development of technology, the possibility of using AI language models in various fields is drawing attention. This study attempted to explore ways to utilize the AI language model in terms of pedagogical practice and research in mathematics education. To this end, the matching rate between the meaning of the Korean dictionary and the mathematical term presented by the AI language model was compared and analyzed using ChatGPT. ChatGPT is one of the AI language models that can process natural language and organize answers according to input text. As a result of the analysis, it was possible to explore the various possibilities of using AI language models for teaching and learning design, evaluation, and research from the basis of answers and analysis of Korean dictionaries and AI language models. However, considering the volatility of the AI language model, it is required to find a way to secure the validity and reliability of the research.

AI-Generated Information for Vascular Patients: Assessing the Standard of Procedure-Specific Information Provided by the ChatGPT AI-Language Model.

Introduction Ensuring access to high-quality information is paramount to facilitating informed surgical decision-making. The use of the internet to access health-related information is increasing, along with the growing prevalence of AI language models such as ChatGPT. We aim to assess the standard of AI-generated patient-facing information through a qualitative analysis of its readability and quality. Materials and methods We performed a retrospective qualitative analysis of information regarding three common vascular procedures: endovascular aortic repair (EVAR), endovenous laser ablation (EVLA), and femoro-popliteal bypass (FPBP). The ChatGPT responses were compared to patient information leaflets provided by the vascular charity, Circulation Foundation UK. Readability was assessed using four readability scores: the Flesch-Kincaid reading ease (FKRE) score, the Flesch-Kincaid grade level (FKGL), the Gunning fog score (GFS), and the simple measure of gobbledygook (SMOG) index. Quality was assessed using the DISCERN tool by two independent assessors. Results The mean FKRE score was 33.3, compared to 59.1 for the information provided by the Circulation Foundation (SD=14.5, p=0.025) indicating poor readability of AI-generated information. The FFKGL indicated that the expected grade of students likely to read and understand ChatGPT responses was consistently higher than compared to information leaflets at 12.7 vs. 9.4 (SD=1.9, p=0.002). Two metrics measure readability in terms of the number of years of education required to understand a piece of writing: the GFS and SMOG. Both scores indicated that AI-generated answers were less accessible. The GFS for ChatGPT-provided information was 16.7 years versus 12.8 years for the leaflets (SD=2.2, p=0.002) and the SMOG index scores were 12.2 and 9.4 years for ChatGPT and the patient information leaflets, respectively (SD=1.7, p=0.001). The DISCERN scores were consistently higher in human-generated patient information leaflets compared to AI-generated information across all procedures; the mean score for the information provided by ChatGPT was 50.3 vs. 56.0 for the Circulation Foundation information leaflets (SD=3.38, p<0.001). Conclusion We concluded that AI-generated information about vascular surgical procedures is currently poor in both the readability of text and the quality of information. Patients should be directed to reputable, human-generated information sources from trusted professional bodies to supplement direct education from the clinician during the pre-procedure consultation process.

Transforming Gambling Harm Reduction in Youth: Leveraging AI Language Models for Personalized Intervention and Prevention

This article highlights the pressing issue of youth gambling addiction, with up to 20% of young individuals engaging in gambling activities, particularly online. With 6-9% of US youth facing gambling problems. This study explores how Large Language Models (LLMs) can enhance harm reduction by providing personalized support, early intervention, and education while respecting privacy. The article draws insights from a review of 45 studies. It was found that youth gambling addiction is a global issue exacerbated by technology and the pandemic. LLMs like GPT-4 offer promise in harm reduction by providing information and support. LLMs can provide vital insights, educational materials, and support for responsible gambling. The article acknowledges potential LLM risks and emphasizes a cautious approach. Recommendations include personalized interventions, online pop-ups, enhanced LLMs, ongoing research, multi-stakeholder collaboration, etc.

ChatGPT and medicine: how AI language models are shaping the future and health related careers.

ChatGPT and medicine: how AI language models are shaping the future and health related careers.

ChatGPT-3.5 as writing assistance in students’ essays

ChatGPT-3.5, an AI language model capable of text generation, translation, summarization, and question-answering, has recently been released for public use. Studies have shown it can generate abstracts, research papers, and dissertations, and create quality essays on different topics. This led to ethical issues in using ChatGPT in academic writing, AI authorship, and evaluating students’ essays. However, it is still unknown how ChatGPT performs in students’ environments as a writing assistant tool and if it enhances students’ essay-writing performance. In the present study, we examined students’ essay-writing performances with or without ChatGPT as an essay-writing assistance tool. The average essay grade was C for both control (traditional essay-writing, n = 9) and experimental (ChatGPT-assisted essay-writing, n = 9) groups. None of the predictors affected essay scores: group, writing duration, study module, and GPA. The text unauthenticity was slightly higher in the experimental group, but the similarity among essays was generally low in the overall sample. In the experimental group, the AI classifier recognized more potential AI-generated texts. Our results demonstrate that the ChatGPT group did not perform better in either of the indicators; the students did not deliver higher quality content, did not write faster, nor had a higher degree of authentic text. We anticipate that these results can relieve some concerns about this tool’s usage in academic writing. ChatGPT-assisted writing could depend on the previous knowledge and skills of the user, which might, in certain instances, lead to confusion in inexperienced users and result in poorer essay writing performance.

Factors affecting accounting students’ misuse of chatgpt: an application of the fraud triangle theory

PurposeThis study aims to explore the factors that contribute to student academic dishonesty through an examination of the misuse of AI language models. Using the fraud triangle theory, which posits that opportunity, rationalization and pressure are key factors for fraudulent behavior, this study investigates how these elements interact and contribute to academic dishonesty among students.Design/methodology/approachIn this study, data on how accounting students used ChatGPT to cheat was acquired from 279 accounting students in Jordanian public universities over the course of two months, from January 2023 to March 2023, through previously tested and validated questionnaires. The main tool for gathering data was a questionnaire distributed online using Microsoft Forms.FindingsThe results show that all of the fraud triangle factors are significant determinants of student academic dishonesty and student misuse of ChatGPT. The findings of this research can be used to guide the development of technology-based preventative measures.Originality/valueThis study provides valuable insights into the motivations and factors that drive students to engage in academic dishonesty and sheds light on the broader issue of technology-assisted academic dishonesty and its impact on the educational system. This study’s contribution is significant, as it sheds light on a pressing issue in education and provides valuable information for educators and policymakers to address the problem and improve academic standards.

“Chatting with ChatGPT”: Analyzing the factors influencing users' intention to Use the Open AI's ChatGPT using the UTAUT model

Open AI's ChatGPT has emerged as a popular AI language model that can engage in natural language conversations with users. Based on a qualitative research approach using semistructured interviews with 32 ChatGPT users from India, this study examined the factors influencing users' acceptance and use of ChatGPT using the unified theory of acceptance and usage of technology (UTAUT) model. The study results demonstrated that the four factors of UTAUT, along with two extended constructs, i.e. perceived interactivity and privacy concerns, can explain users' interaction and engagement with ChatGPT. The study also found that age and experience can moderate the impact of various factors on the use of ChatGPT. The theoretical and practical implications of the study were also discussed.

A New Era for Data Analysis in Qualitative Research: ChatGPT!

Today, the use of software in qualitative research analysis is rapidly becoming widespread among researchers. Researchers manage large data sets using features such as editing data, transcribating, creating codes, and searching within data. However, while the data analysis uses software in a format, the analysis of the essence of the data is done by researchers. An AI language model, ChatGPT, released by the OpenAI company, has features such as text editing, creation, and abbreviation. In this research, considering the characteristics of ChatGPT, an answer was sought to the question of whether it can be used in data analysis for qualitative research. In this research, the case study of the qualitative research method was preferred. The data of the research consists of interview texts of two participants from an unpublished study. The texts were subjected to qualitative research analysis process through ChatGPT-4. Data analysis was done in two separate ways, specifying code, category and theme and not specifying. In conclusion, it has been found that ChatGPT can create code, category and theme, cite directly from within the text, interpret data sets, and analyze the meaning at the core of the data sets. In this context, the availability of ChatGPT in data analysis in qualitative research has been discussed.

Applications of AI in Computer Vision and NLP

Computer vision and language AI (or speech AI) are two rapidly growing fields that have the potential to revolutionize the way when interacted with technology. When it comes to artificial intelligence, there are two main branches: computer vision, which focuses on visual data, and language AI (or voice AI), which focuses on both written and spoken language. The purpose of this research is to offer a snapshot of where things stand in this area right now, highlighting recent research and key findings in both computer vision and language AI (or speech AI). The literature review will focus on the most recent advancements and trends in these fields, as well as identify gaps in the literature that need further research.

Literature Review: Recent Advances in Computer Vision and Language AI

This comprehensive literature review examines the latest breakthroughs in computer vision and natural language processing (NLP), two rapidly evolving fields with applications across search, human-computer interaction, robotics, and more. It synthesizes key findings, trends, limitations, and open challenges from cutting-edge research at their intersection. The dramatic progress driven by deep neural networks is analysed in depth, along with issues like generalization, context handling, reasoning, uncertainty, and human-centric evaluation. Although remarkable advances have been made, especially in computer vision, core problems remain to be addressed. This review provides a thorough overview of the state-of-the-art, reflecting the most recent innovations, and promising future directions in this dynamic research domain.

Application of ChatGPT in Higher Education and Research – A Futuristic Analysis

Purpose: The purpose of conducting research on the "Application of ChatGPT in Higher Education and Research – A Futuristic Analysis" is to critically examine the evolving role of advanced AI language models like ChatGPT in shaping the future of education and research. This research seeks to anticipate how ChatGPT and similar technologies will impact pedagogy, academic support, and scholarly inquiry in the years ahead, shedding light on their potential benefits and challenges. By analyzing current implementations and forecasting future possibilities, this research aims to inform educators, institutions, and researchers about the transformative opportunities and ethical considerations associated with the integration of AI-driven chatbots and language models in higher education and research settings. Methodology: This is exploratory research and makes use of the information obtained from scholarly articles through Google Scholar and AI-based GPTs to analyse, compare, evaluate, and interpret the concept of application of ChatGPT in Higher Education and Research. Results/Analysis: A systematic analysis is carried out on the futuristic and effective use of ChatGPT for higher education, advanced research, scholarly publication, and possible threats of it on higher education industry. Originality/Value: A systematic analysis is carried out to interpret: (1) the diverse applications of ChatGPT in various academic disciplines, including basic sciences, engineering, health sciences, agriculture, management, and social sciences within higher education, (2) how ChatGPT contributes to different types of research, including exploratory, empirical, and experimental research endeavours. Type of Paper: Exploratory Research.

Emergent local structures in an ecosystem of social bots and humans on Twitter

Bots in online social networks can be used for good or bad but their presence is unavoidable and will increase in the future. To investigate how the interaction networks of bots and humans evolve, we created six social bots on Twitter with AI language models and let them carry out standard user operations. Three different strategies were implemented for the bots: a trend-targeting strategy (TTS), a keywords-targeting strategy (KTS) and a user-targeting strategy (UTS). We examined the interaction patterns such as targeting users, spreading messages, propagating relationships, and engagement. We focused on the emergent local structures or motifs and found that the strategies of the social bots had a significant impact on them. Motifs resulting from interactions with bots following TTS or KTS are simple and show significant overlap, while those resulting from interactions with UTS-governed bots lead to more complex motifs. These findings provide insights into human-bot interaction patterns in online social networks, and can be used to develop more effective bots for beneficial tasks and to combat malicious actors.

Children of AI: A Protocol for Managing the Born-Digital Ephemera Spawned by Generative AI Language Models

The recent public release of the generative AI language model ChatGPT has captured the public imagination and has resulted in a rapid uptake and widespread experimentation by the general public and academia alike. The number of academic publications focusing on the capabilities as well as practical and ethical implications of generative AI has been growing exponentially. One of the concerns with this unprecedented growth in scholarship related to generative AI, in particular, ChatGPT, is that, in most cases, the raw data, which is the text of the original ‘conversations,’ have not been made available to the audience of the papers and thus cannot be drawn on to assess the veracity of the arguments made and the conclusions drawn therefrom. This paper provides a protocol for the documentation and archiving of these raw data.