Displaying publications 21 - 40 of 376 in total

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  1. Fulltext Can Building "Artificially Intelligent Cities" Safeguard Humanity from Natural Disasters, Pandemics, and Other Catastrophes? An Urban Scholar's Perspective
    Yigitcanlar T, Butler L, Windle E, Desouza KC, Mehmood R, Corchado JM
    Sensors (Basel), 2020 May 25;20(10).
    PMID: 32466175 DOI: 10.3390/s20102988
    In recent years, artificial intelligence (AI) has started to manifest itself at an unprecedented pace. With highly sophisticated capabilities, AI has the potential to dramatically change our cities and societies. Despite its growing importance, the urban and social implications of AI are still an understudied area. In order to contribute to the ongoing efforts to address this research gap, this paper introduces the notion of an artificially intelligent city as the potential successor of the popular smart city brand-where the smartness of a city has come to be strongly associated with the use of viable technological solutions, including AI. The study explores whether building artificially intelligent cities can safeguard humanity from natural disasters, pandemics, and other catastrophes. All of the statements in this viewpoint are based on a thorough review of the current status of AI literature, research, developments, trends, and applications. This paper generates insights and identifies prospective research questions by charting the evolution of AI and the potential impacts of the systematic adoption of AI in cities and societies. The generated insights inform urban policymakers, managers, and planners on how to ensure the correct uptake of AI in our cities, and the identified critical questions offer scholars directions for prospective research and development.
    Matched MeSH terms: Artificial Intelligence*
  2. Fulltext Modern Trends and Applications of Intelligent Methods in Biomedical Signal and Image Processing
    Kubicek J, Penhaker M, Krejcar O, Selamat A
    Sensors (Basel), 2021 Jan 27;21(3).
    PMID: 33513910 DOI: 10.3390/s21030847
    There are various modern systems for the measurement and consequent acquisition of valuable patient's records in the form of medical signals and images, which are supposed to be processed to provide significant information about the state of biological tissues [...].
    Matched MeSH terms: Artificial Intelligence*
  3. Artificial intelligence in drug development: present status and future prospects
    Mak KK, Pichika MR
    Drug Discov Today, 2019 03;24(3):773-780.
    PMID: 30472429 DOI: 10.1016/j.drudis.2018.11.014
    Artificial intelligence (AI) uses personified knowledge and learns from the solutions it produces to address not only specific but also complex problems. Remarkable improvements in computational power coupled with advancements in AI technology could be utilised to revolutionise the drug development process. At present, the pharmaceutical industry is facing challenges in sustaining their drug development programmes because of increased R&D costs and reduced efficiency. In this review, we discuss the major causes of attrition rates in new drug approvals, the possible ways that AI can improve the efficiency of the drug development process and collaboration of pharmaceutical industry giants with AI-powered drug discovery firms.
    Matched MeSH terms: Artificial Intelligence*
  4. Interpretable and explainable AI (XAI) model for spatial drought prediction
    Dikshit A, Pradhan B
    Sci Total Environ, 2021 Dec 20;801:149797.
    PMID: 34467917 DOI: 10.1016/j.scitotenv.2021.149797
    Accurate prediction of any type of natural hazard is a challenging task. Of all the various hazards, drought prediction is challenging as it lacks a universal definition and is getting adverse with climate change impacting drought events both spatially and temporally. The problem becomes more complex as drought occurrence is dependent on a multitude of factors ranging from hydro-meteorological to climatic variables. A paradigm shift happened in this field when it was found that the inclusion of climatic variables in the data-driven prediction model improves the accuracy. However, this understanding has been primarily using statistical metrics used to measure the model accuracy. The present work tries to explore this finding using an explainable artificial intelligence (XAI) model. The explainable deep learning model development and comparative analysis were performed using known understandings drawn from physical-based models. The work also tries to explore how the model achieves specific results at different spatio-temporal intervals, enabling us to understand the local interactions among the predictors for different drought conditions and drought periods. The drought index used in the study is Standard Precipitation Index (SPI) at 12 month scales applied for five different regions in New South Wales, Australia, with the explainable algorithm being SHapley Additive exPlanations (SHAP). The conclusions drawn from SHAP plots depict the importance of climatic variables at a monthly scale and varying ranges of annual scale. We observe that the results obtained from SHAP align with the physical model interpretations, thus suggesting the need to add climatic variables as predictors in the prediction model.
    Matched MeSH terms: Artificial Intelligence*
  5. Explainable deep learning ensemble for food image analysis on edge devices
    Tahir GA, Loo CK
    Comput Biol Med, 2021 12;139:104972.
    PMID: 34749093 DOI: 10.1016/j.compbiomed.2021.104972
    Food recognition systems recently garnered much research attention in the relevant field due to their ability to obtain objective measurements for dietary intake. This feature contributes to the management of various chronic conditions. Challenges such as inter and intraclass variations alongside the practical applications of smart glasses, wearable cameras, and mobile devices require resource-efficient food recognition models with high classification performance. Furthermore, explainable AI is also crucial in health-related domains as it characterizes model performance, enhancing its transparency and objectivity. Our proposed architecture attempts to address these challenges by drawing on the strengths of the transfer learning technique upon initializing MobiletNetV3 with weights from a pre-trained model of ImageNet. The MobileNetV3 achieves superior performance using the squeeze and excitation strategy, providing unequal weight to different input channels and contrasting equal weights in other variants. Despite being fast and efficient, there is a high possibility for it to be stuck in the local optima like other deep neural networks, reducing the desired classification performance of the model. Thus, we overcome this issue by applying the snapshot ensemble approach as it enables the M model in a single training process without any increase in the required training time. As a result, each snapshot in the ensemble visits different local minima before converging to the final solution which enhances recognition performance. On overcoming the challenge of explainability, we argue that explanations cannot be monolithic, since each stakeholder perceive the results', explanations based on different objectives and aims. Thus, we proposed a user-centered explainable artificial intelligence (AI) framework to increase the trust of the involved parties by inferencing and rationalizing the results according to needs and user profile. Our framework is comprehensive in terms of a dietary assessment app as it detects Food/Non-Food, food categories, and ingredients. Experimental results on the standard food benchmarks and newly contributed Malaysian food dataset for ingredient detection demonstrated superior performance on an integrated set of measures over other methodologies.
    Matched MeSH terms: Artificial Intelligence*
  6. Fulltext Predicting the natural history of artificial intelligence in travel medicine
    Flaherty GT, Piyaphanee W
    J Travel Med, 2023 Feb 18;30(1).
    PMID: 36208173 DOI: 10.1093/jtm/taac113
    Matched MeSH terms: Artificial Intelligence*
  7. Modeling of carbon dioxide fixation by microalgae using hybrid artificial intelligence (AI) and fuzzy logic (FL) methods and optimization by genetic algorithm (GA)
    Kushwaha OS, Uthayakumar H, Kumaresan K
    Environ Sci Pollut Res Int, 2023 Feb;30(10):24927-24948.
    PMID: 35349067 DOI: 10.1007/s11356-022-19683-0
    In this study, we are reporting a novel prediction model for forecasting the carbon dioxide (CO2) fixation of microalgae which is based on the hybrid approach of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA). The CO2 fixation rate of various algal strains was collected and the cultivation conditions of the microalgae such as temperature, pH, CO2 %, and amount of nitrogen and phosphorous (mg/L) were taken as the input variables, while the CO2 fixation rate was taken as the output variable. The optimization of ANFIS parameters and the formation of the optimized fuzzy model structure were performed by genetic algorithm (GA) using MATLAB in order to achieve optimum prediction capability and industrial applicability. The best-fitting model was figured out using statistical analysis parameters such as root mean square error (RMSE), coefficient of regression (R2), and average absolute relative deviation (AARD). According to the analysis, GA-ANFIS model depicted a greater prediction capability over ANFIS model. The RMSE, R2, and AARD for GA-ANFIS were observed to be 0.000431, 0.97865, and 0.044354 in the training phase and 0.00056, 0.98457, and 0.032156 in the testing phase, respectively, for the GA-ANFIS Model. As a result, it can be concluded that the proposed GA-ANFIS model is an efficient technique having a very high potential to accurately predict the CO2 fixation rate.
    Matched MeSH terms: Artificial Intelligence*
  8. No person is an island: Unpacking the work and after-work consequences of interacting with artificial intelligence
    Tang PM, Koopman J, Mai KM, De Cremer D, Zhang JH, Reynders P, et al.
    J Appl Psychol, 2023 Nov;108(11):1766-1789.
    PMID: 37307359 DOI: 10.1037/apl0001103
    The artificial intelligence (AI) revolution has arrived, as AI systems are increasingly being integrated across organizational functions into the work lives of employees. This coupling of employees and machines fundamentally alters the work-related interactions to which employees are accustomed, as employees find themselves increasingly interacting with, and relying on, AI systems instead of human coworkers. This increased coupling of employees and AI portends a shift toward more of an "asocial system," wherein people may feel socially disconnected at work. Drawing upon the social affiliation model, we develop a model delineating both adaptive and maladaptive consequences of this situation. Specifically, we theorize that the more employees interact with AI in the pursuit of work goals, the more they experience a need for social affiliation (adaptive)-which may contribute to more helping behavior toward coworkers at work-as well as a feeling of loneliness (maladaptive), which then further impair employee well-being after work (i.e., more insomnia and alcohol consumption). In addition, we submit that these effects should be especially pronounced among employees with higher levels of attachment anxiety. Results across four studies (N = 794) with mixed methodologies (i.e., survey study, field experiment, and simulation study; Studies 1-4) with employees from four different regions (i.e., Taiwan, Indonesia, United States, and Malaysia) generally support our hypotheses. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
    Matched MeSH terms: Artificial Intelligence*
  9. Fulltext Next-generation nanophotonic-enabled biosensors for intelligent diagnosis of SARS-CoV-2 variants
    Taha BA, Al Mashhadany Y, Al-Jubouri Q, Rashid ARBA, Luo Y, Chen Z, et al.
    Sci Total Environ, 2023 Jul 01;880:163333.
    PMID: 37028663 DOI: 10.1016/j.scitotenv.2023.163333
    Constantly mutating SARS-CoV-2 is a global concern resulting in COVID-19 infectious waves from time to time in different regions, challenging present-day diagnostics and therapeutics. Early-stage point-of-care diagnostic (POC) biosensors are a crucial vector for the timely management of morbidity and mortalities caused due to COVID-19. The state-of-the-art SARS-CoV-2 biosensors depend upon developing a single platform for its diverse variants/biomarkers, enabling precise detection and monitoring. Nanophotonic-enabled biosensors have emerged as 'one platform' to diagnose COVID-19, addressing the concern of constant viral mutation. This review assesses the evolution of current and future variants of the SARS-CoV-2 and critically summarizes the current state of biosensor approaches for detecting SARS-CoV-2 variants/biomarkers employing nanophotonic-enabled diagnostics. It discusses the integration of modern-age technologies, including artificial intelligence, machine learning and 5G communication with nanophotonic biosensors for intelligent COVID-19 monitoring and management. It also highlights the challenges and potential opportunities for developing intelligent biosensors for diagnosing future SARS-CoV-2 variants. This review will guide future research and development on nano-enabled intelligent photonic-biosensor strategies for early-stage diagnosing of highly infectious diseases to prevent repeated outbreaks and save associated human mortalities.
    Matched MeSH terms: Artificial Intelligence; Intelligence
  10. Fulltext Effectiveness of Artificial Intelligence (AI) in Improving Pupils' Deep Learning in Primary School Mathematics Teaching in Fujian Province
    Qiu Y, Pan J, Ishak NA
    Comput Intell Neurosci, 2022;2022:1362996.
    PMID: 36193186 DOI: 10.1155/2022/1362996
    Several primary school students in Fujian Province have perceived studying mathematics as challenging. To deal with this issue, computer technology advancements, specifically artificial intelligence (AI), present an opportunity to evaluate individual students' learning challenges and give individualized support to optimize their success in mathematics classes. It is also possible to use virtual reality (VR) to assist learners in acquiring complex mathematical and logical ideas and to lessen learners' mistakes. As a result, researchers, particularly beginners, are missing out on a complete perspective of the study of AI in teaching mathematics. That is why we are exploring the role of AI in math education by developing a "fuzzy-based tweakable convolution neural network with a long short-term memory (FT-CNN-LSTM-AM)" method. For this investigation, the students' datasets are taken and educated by mathematical teaching via the application of AI. The proposed method is utilized to predict the students' performance in mathematical education. A grey wolf optimizer is employed to boost the effectiveness of the proposed method. Furthermore, the performance of the proposed method is analyzed and compared with existing approaches to gain the highest reliability.
    Matched MeSH terms: Artificial Intelligence*
  11. Fulltext Blockchain and Machine Learning Inspired Secure Smart Home Communication Network
    Menon S, Anand D, Kavita, Verma S, Kaur M, Jhanjhi NZ, et al.
    Sensors (Basel), 2023 Jul 04;23(13).
    PMID: 37447981 DOI: 10.3390/s23136132
    With the increasing growth rate of smart home devices and their interconnectivity via the Internet of Things (IoT), security threats to the communication network have become a concern. This paper proposes a learning engine for a smart home communication network that utilizes blockchain-based secure communication and a cloud-based data evaluation layer to segregate and rank data on the basis of three broad categories of Transactions (T), namely Smart T, Mod T, and Avoid T. The learning engine utilizes a neural network for the training and classification of the categories that helps the blockchain layer with improvisation in the decision-making process. The contributions of this paper include the application of a secure blockchain layer for user authentication and the generation of a ledger for the communication network; the utilization of the cloud-based data evaluation layer; the enhancement of an SI-based algorithm for training; and the utilization of a neural engine for the precise training and classification of categories. The proposed algorithm outperformed the Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system, the data fusion technique, and artificial intelligence Internet of Things technology in providing electronic information engineering and analyzing optimization schemes in terms of the computation complexity, false authentication rate, and qualitative parameters with a lower average computation complexity; in addition, it ensures a secure, efficient smart home communication network to enhance the lifestyle of human beings.
    Matched MeSH terms: Artificial Intelligence*
  12. Techno-economic optimization of a new waste-to-energy plant for electricity, cooling, and desalinated water using various biomass for emission reduction
    Hai T, Ma X, Singh Chauhan B, Mahmoud S, Al-Kouz W, Tong J, et al.
    Chemosphere, 2023 Oct;338:139398.
    PMID: 37406939 DOI: 10.1016/j.chemosphere.2023.139398
    A newly developed waste-to-energy system using a biomass combined energy system designed and taken into account for electricity generation, cooling, and freshwater production has been investigated and modeled in this project. The investigated system incorporates several different cycles, such as a biomass waste integrated gasifier-gas turbine cycle, a high-temperature fuel cell, a Rankine cycle, an absorption refrigeration system, and a flash distillation system for seawater desalination. The EES software is employed to perform a basic analysis of the system. They are then transferred to MATLAB software to optimize and evaluate the impact of operational factors. Artificial intelligence is employed to evaluate and model the EES software's analysis output for this purpose. By enhancing the flow rate of fuel from 4 to 6.5 kg/s, the cost rate and energy efficiency are reduced by 51% and increased by 6.5%, respectively. Furthermore, the maximum increment in exergetic efficiency takes place whenever the inlet temperature of the gas turbine rises. According to an analysis of three types of biomasses, Solid Waste possesses the maximum efficiency rate, work output, and expense. Rice Husk, in contrast, has the minimum efficiency, work output, and expense. Additionally, with the change in fuel discharge and gas turbine inlet temperature, the system behavior for all three types of biomasses will be nearly identical. The Pareto front optimization findings demonstrate that the best mode for system performance is an output power of 53,512 kW, a cost of 0.643 dollars per second, and a first law efficiency of 42%. This optimal value occurs for fuel discharge of 5.125 and the maximum inlet temperature for a gas turbine. The rates of water desalination and cooling in this condition are 18.818 kg/s and 2356 kW, respectively.
    Matched MeSH terms: Artificial Intelligence*
  13. A Tête-à-tête with ChatGPT on the impact of artificial intelligence in medical education
    Vignesh R, Pradeep P, Balakrishnan P
    Med J Malaysia, 2023 Jul;78(4):547-549.
    PMID: 37518931
    Chat Generative Pre-Trained Transformer (ChatGPT) is an artificial intelligence (AI) language model developed by OpenAI. It is trained to process vast amounts of text and engage in human-like conversational interaction with users. Being accessible by all, it is widely used and its capabilities range from language translation, summarising long texts and creative writing. This article explores the potential role of ChatGPT in medical education and the possible concerns about the misuse of this technology through a conversation with ChatGPT itself via text prompts. The implications of this technology in medical education as told by ChatGPT are interesting and seemingly helpful for both the students and the tutors. However, this could be a double-edged sword considering the risks of compromised students' integrity and concerns of over-reliance. This also calls for counter strategies and policies in place to mitigate these risks.
    Matched MeSH terms: Artificial Intelligence*
  14. Using artificial intelligence methods for systematic review in health sciences: A systematic review
    Blaizot A, Veettil SK, Saidoung P, Moreno-Garcia CF, Wiratunga N, Aceves-Martins M, et al.
    Res Synth Methods, 2022 May;13(3):353-362.
    PMID: 35174972 DOI: 10.1002/jrsm.1553
    The exponential increase in published articles makes a thorough and expedient review of literature increasingly challenging. This review delineated automated tools and platforms that employ artificial intelligence (AI) approaches and evaluated the reported benefits and challenges in using such methods. A search was conducted in 4 databases (Medline, Embase, CDSR, and Epistemonikos) up to April 2021 for systematic reviews and other related reviews implementing AI methods. To be included, the review must use any form of AI method, including machine learning, deep learning, neural network, or any other applications used to enable the full or semi-autonomous performance of one or more stages in the development of evidence synthesis. Twelve reviews were included, using nine different tools to implement 15 different AI methods. Eleven methods were used in the screening stages of the review (73%). The rest were divided: two in data extraction (13%) and two in risk of bias assessment (13%). The ambiguous benefits of the data extractions, combined with the reported advantages from 10 reviews, indicating that AI platforms have taken hold with varying success in evidence synthesis. However, the results are qualified by the reliance on the self-reporting of the review authors. Extensive human validation still appears required at this stage in implementing AI methods, though further evaluation is required to define the overall contribution of such platforms in enhancing efficiency and quality in evidence synthesis.
    Matched MeSH terms: Artificial Intelligence*
  15. Fulltext The Potential of Chatbots for Emotional Support and Promoting Mental Well-Being in Different Cultures: Mixed Methods Study
    Chin H, Song H, Baek G, Shin M, Jung C, Cha M, et al.
    J Med Internet Res, 2023 Oct 20;25:e51712.
    PMID: 37862063 DOI: 10.2196/51712
    BACKGROUND: Artificial intelligence chatbot research has focused on technical advances in natural language processing and validating the effectiveness of human-machine conversations in specific settings. However, real-world chat data remain proprietary and unexplored despite their growing popularity, and new analyses of chatbot uses and their effects on mitigating negative moods are urgently needed.

    OBJECTIVE: In this study, we investigated whether and how artificial intelligence chatbots facilitate the expression of user emotions, specifically sadness and depression. We also examined cultural differences in the expression of depressive moods among users in Western and Eastern countries.

    METHODS: This study used SimSimi, a global open-domain social chatbot, to analyze 152,783 conversation utterances containing the terms "depress" and "sad" in 3 Western countries (Canada, the United Kingdom, and the United States) and 5 Eastern countries (Indonesia, India, Malaysia, the Philippines, and Thailand). Study 1 reports new findings on the cultural differences in how people talk about depression and sadness to chatbots based on Linguistic Inquiry and Word Count and n-gram analyses. In study 2, we classified chat conversations into predefined topics using semisupervised classification techniques to better understand the types of depressive moods prevalent in chats. We then identified the distinguishing features of chat-based depressive discourse data and the disparity between Eastern and Western users.

    RESULTS: Our data revealed intriguing cultural differences. Chatbot users in Eastern countries indicated stronger emotions about depression than users in Western countries (positive: P

    Matched MeSH terms: Artificial Intelligence*
  16. The Potential of ChatGPT in Assisting Children with Down Syndrome
    Yang J, Por LY, Leong MC, Ku CS
    Ann Biomed Eng, 2023 Dec;51(12):2638-2640.
    PMID: 37332002 DOI: 10.1007/s10439-023-03281-3
    ChatGPT, an advanced language generation model developed by OpenAI, has the potential to revolutionize healthcare delivery and support for individuals with various conditions, including Down syndrome. This article explores the applications of ChatGPT in assisting children with Down syndrome, highlighting the benefits it can bring to their education, social interaction, and overall well-being. While acknowledging the challenges and limitations, we examine how ChatGPT can be utilized as a valuable tool in enhancing the lives of these children, promoting their cognitive development, and supporting their unique needs.
    Matched MeSH terms: Artificial Intelligence*
  17. New insights in improving sustainability in meat production: opportunities and challenges
    Kumar P, Abubakar AA, Verma AK, Umaraw P, Adewale Ahmed M, Mehta N, et al.
    Crit Rev Food Sci Nutr, 2023 Nov;63(33):11830-11858.
    PMID: 35821661 DOI: 10.1080/10408398.2022.2096562
    Treating livestock as senseless production machines has led to rampant depletion of natural resources, enhanced greenhouse gas emissions, gross animal welfare violations, and other ethical issues. It has essentially instigated constant scrutiny of conventional meat production by various experts and scientists. Sustainably in the meat sector is a big challenge which requires a multifaced and holistic approach. Novel tools like digitalization of the farming system and livestock market, precision livestock farming, application of remote sensing and artificial intelligence to manage production and environmental impact/GHG emission, can help in attaining sustainability in this sector. Further, improving nutrient use efficiency and recycling in feed and animal production through integration with agroecology and industrial ecology, improving individual animal and herd health by ensuring proper biosecurity measures and selective breeding, and welfare by mitigating animal stress during production are also key elements in achieving sustainability in meat production. In addition, sustainability bears a direct relationship with various social dimensions of meat production efficiency such as non-market attributes, balance between demand and consumption, market and policy failures. The present review critically examines the various aspects that significantly impact the efficiency and sustainability of meat production.
    Matched MeSH terms: Artificial Intelligence*
  18. Fulltext Artificial intelligence and the orthopaedic surgeon: A review of the literature and potential applications for future practice: Current concepts
    Khoriati AA, Shahid Z, Fok M, Frank RM, Voss A, D'Hooghe P, et al.
    J ISAKOS, 2024 Apr;9(2):227-233.
    PMID: 37949113 DOI: 10.1016/j.jisako.2023.10.015
    Matched MeSH terms: Artificial Intelligence*
  19. Emergence of Artificial Intelligence Art Therapies (AIATs) in Mental Health Care: A Systematic Review
    Luo X, Zhang A, Li Y, Zhang Z, Ying F, Lin R, et al.
    Int J Ment Health Nurs, 2024 Dec;33(6):1743-1760.
    PMID: 39020473 DOI: 10.1111/inm.13384
    The application of artificial intelligence art therapies (AIATs) in mental health care represents an innovative merger between digital technology and the therapeutic potential of creative arts. This systematic review aimed to assess the effectiveness and ethical considerations of AIATs, incorporating robots, AI painting and AI Chatbots to augment traditional art therapies. Aligning with the Preferred Reporting Items for systematic reviews (PRISMA) guidelines, we meticulously searched PubMed, Cochrane Library, Web of Science and CNKI, resulting in 15 selected articles for detailed analysis. To ensure methodological quality, we applied the Joanna Briggs Institute (JBI) criteria for quality assessment and extracted data using the PICO(S) format, specifically targeting randomised controlled trials (RCTs). Our findings suggest that AIATs can profoundly enhance the therapeutic experience by providing new creative outlets and reinforcing existing methods, despite possible drawbacks and ethical challenges. This examination underscores AIATs' potential to enrich mental health therapies, emphasising the critical importance of ethical considerations and the responsible application of AI as the field evolves. With a focus on expanding treatment efficacy and patient expressiveness, the promise of AIATs in mental health care necessitates a careful balance between innovation and ethical responsibility. Trial Registration: PROSPERO: CRD42024504472.
    Matched MeSH terms: Artificial Intelligence*
  20. Fulltext A multinational study on the factors influencing university students' attitudes and usage of ChatGPT
    Abdaljaleel M, Barakat M, Alsanafi M, Salim NA, Abazid H, Malaeb D, et al.
    Sci Rep, 2024 Jan 23;14(1):1983.
    PMID: 38263214 DOI: 10.1038/s41598-024-52549-8
    Artificial intelligence models, like ChatGPT, have the potential to revolutionize higher education when implemented properly. This study aimed to investigate the factors influencing university students' attitudes and usage of ChatGPT in Arab countries. The survey instrument "TAME-ChatGPT" was administered to 2240 participants from Iraq, Kuwait, Egypt, Lebanon, and Jordan. Of those, 46.8% heard of ChatGPT, and 52.6% used it before the study. The results indicated that a positive attitude and usage of ChatGPT were determined by factors like ease of use, positive attitude towards technology, social influence, perceived usefulness, behavioral/cognitive influences, low perceived risks, and low anxiety. Confirmatory factor analysis indicated the adequacy of the "TAME-ChatGPT" constructs. Multivariate analysis demonstrated that the attitude towards ChatGPT usage was significantly influenced by country of residence, age, university type, and recent academic performance. This study validated "TAME-ChatGPT" as a useful tool for assessing ChatGPT adoption among university students. The successful integration of ChatGPT in higher education relies on the perceived ease of use, perceived usefulness, positive attitude towards technology, social influence, behavioral/cognitive elements, low anxiety, and minimal perceived risks. Policies for ChatGPT adoption in higher education should be tailored to individual contexts, considering the variations in student attitudes observed in this study.
    Matched MeSH terms: Artificial Intelligence*
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