Displaying publications 81 - 100 of 375 in total

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  1. Intellectual development of Malaysian medical students: a follow-up study
    Coomaraswamy SD
    Br J Med Educ, 1974 Sep;8(3):187-91.
    PMID: 4421860
    Matched MeSH terms: Intelligence*; Intelligence Tests
  2. Fulltext Application of reinforcement learning in cognitive radio networks: models and algorithms
    Yau KL, Poh GS, Chien SF, Al-Rawi HA
    ScientificWorldJournal, 2014;2014:209810.
    PMID: 24995352 DOI: 10.1155/2014/209810
    Cognitive radio (CR) enables unlicensed users to exploit the underutilized spectrum in licensed spectrum whilst minimizing interference to licensed users. Reinforcement learning (RL), which is an artificial intelligence approach, has been applied to enable each unlicensed user to observe and carry out optimal actions for performance enhancement in a wide range of schemes in CR, such as dynamic channel selection and channel sensing. This paper presents new discussions of RL in the context of CR networks. It provides an extensive review on how most schemes have been approached using the traditional and enhanced RL algorithms through state, action, and reward representations. Examples of the enhancements on RL, which do not appear in the traditional RL approach, are rules and cooperative learning. This paper also reviews performance enhancements brought about by the RL algorithms and open issues. This paper aims to establish a foundation in order to spark new research interests in this area. Our discussion has been presented in a tutorial manner so that it is comprehensive to readers outside the specialty of RL and CR.
    Matched MeSH terms: Artificial Intelligence*
  3. Fulltext Minimum variance distortionless response beamformer with enhanced nulling level control via dynamic mutated artificial immune system
    Kiong TS, Salem SB, Paw JK, Sankar KP, Darzi S
    ScientificWorldJournal, 2014;2014:164053.
    PMID: 25003136 DOI: 10.1155/2014/164053
    In smart antenna applications, the adaptive beamforming technique is used to cancel interfering signals (placing nulls) and produce or steer a strong beam toward the target signal according to the calculated weight vectors. Minimum variance distortionless response (MVDR) beamforming is capable of determining the weight vectors for beam steering; however, its nulling level on the interference sources remains unsatisfactory. Beamforming can be considered as an optimization problem, such that optimal weight vector should be obtained through computation. Hence, in this paper, a new dynamic mutated artificial immune system (DM-AIS) is proposed to enhance MVDR beamforming for controlling the null steering of interference and increase the signal to interference noise ratio (SINR) for wanted signals.
    Matched MeSH terms: Artificial Intelligence*
  4. Fulltext A generalized quantum-inspired decision making model for intelligent agent
    Hu Y, Loo CK
    ScientificWorldJournal, 2014;2014:240983.
    PMID: 24778580 DOI: 10.1155/2014/240983
    A novel decision making for intelligent agent using quantum-inspired approach is proposed. A formal, generalized solution to the problem is given. Mathematically, the proposed model is capable of modeling higher dimensional decision problems than previous researches. Four experiments are conducted, and both empirical experiments results and proposed model's experiment results are given for each experiment. Experiments showed that the results of proposed model agree with empirical results perfectly. The proposed model provides a new direction for researcher to resolve cognitive basis in designing intelligent agent.
    Matched MeSH terms: Intelligence*
  5. Classification of intelligence quotient via brainwave sub-band power ratio features and artificial neural network
    Jahidin AH, Megat Ali MS, Taib MN, Tahir NM, Yassin IM, Lias S
    Comput Methods Programs Biomed, 2014 Apr;114(1):50-9.
    PMID: 24560277 DOI: 10.1016/j.cmpb.2014.01.016
    This paper elaborates on the novel intelligence assessment method using the brainwave sub-band power ratio features. The study focuses only on the left hemisphere brainwave in its relaxed state. Distinct intelligence quotient groups have been established earlier from the score of the Raven Progressive Matrices. Sub-band power ratios are calculated from energy spectral density of theta, alpha and beta frequency bands. Synthetic data have been generated to increase dataset from 50 to 120. The features are used as input to the artificial neural network. Subsequently, the brain behaviour model has been developed using an artificial neural network that is trained with optimized learning rate, momentum constant and hidden nodes. Findings indicate that the distinct intelligence quotient groups can be classified from the brainwave sub-band power ratios with 100% training and 88.89% testing accuracies.
    Matched MeSH terms: Intelligence*
  6. A knowledge creation info-structure to acquire and crystallize the tacit knowledge of health-care experts
    Abidi SS, Cheah YN, Curran J
    IEEE Trans Inf Technol Biomed, 2005 Jun;9(2):193-204.
    PMID: 16138536
    Tacit knowledge of health-care experts is an important source of experiential know-how, yet due to various operational and technical reasons, such health-care knowledge is not entirely harnessed and put into professional practice. Emerging knowledge-management (KM) solutions suggest strategies to acquire the seemingly intractable and nonarticulated tacit knowledge of health-care experts. This paper presents a KM methodology, together with its computational implementation, to 1) acquire the tacit knowledge possessed by health-care experts; 2) represent the acquired tacit health-care knowledge in a computational formalism--i.e., clinical scenarios--that allows the reuse of stored knowledge to acquire tacit knowledge; and 3) crystallize the acquired tacit knowledge so that it is validated for health-care decision-support and medical education systems.
    Matched MeSH terms: Artificial Intelligence*
  7. Fulltext A Biogeography-Based Optimization Algorithm Hybridized with Tabu Search for the Quadratic Assignment Problem
    Lim WL, Wibowo A, Desa MI, Haron H
    Comput Intell Neurosci, 2016;2016:5803893.
    PMID: 26819585 DOI: 10.1155/2016/5803893
    The quadratic assignment problem (QAP) is an NP-hard combinatorial optimization problem with a wide variety of applications. Biogeography-based optimization (BBO), a relatively new optimization technique based on the biogeography concept, uses the idea of migration strategy of species to derive algorithm for solving optimization problems. It has been shown that BBO provides performance on a par with other optimization methods. A classical BBO algorithm employs the mutation operator as its diversification strategy. However, this process will often ruin the quality of solutions in QAP. In this paper, we propose a hybrid technique to overcome the weakness of classical BBO algorithm to solve QAP, by replacing the mutation operator with a tabu search procedure. Our experiments using the benchmark instances from QAPLIB show that the proposed hybrid method is able to find good solutions for them within reasonable computational times. Out of 61 benchmark instances tested, the proposed method is able to obtain the best known solutions for 57 of them.
    Matched MeSH terms: Artificial Intelligence*
  8. RAIRS2 a new expert system for diagnosing tuberculosis with real-world tournament selection mechanism inside artificial immune recognition system
    Saybani MR, Shamshirband S, Golzari S, Wah TY, Saeed A, Mat Kiah ML, et al.
    Med Biol Eng Comput, 2016 Mar;54(2-3):385-99.
    PMID: 26081904 DOI: 10.1007/s11517-015-1323-6
    Tuberculosis is a major global health problem that has been ranked as the second leading cause of death from an infectious disease worldwide, after the human immunodeficiency virus. Diagnosis based on cultured specimens is the reference standard; however, results take weeks to obtain. Slow and insensitive diagnostic methods hampered the global control of tuberculosis, and scientists are looking for early detection strategies, which remain the foundation of tuberculosis control. Consequently, there is a need to develop an expert system that helps medical professionals to accurately diagnose the disease. The objective of this study is to diagnose tuberculosis using a machine learning method. Artificial immune recognition system (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. In order to increase the classification accuracy, this study introduces a new hybrid system that incorporates real tournament selection mechanism into the AIRS. This mechanism is used to control the population size of the model and to overcome the existing selection pressure. Patient epacris reports obtained from the Pasteur laboratory in northern Iran were used as the benchmark data set. The sample consisted of 175 records, from which 114 (65 %) were positive for TB, and the remaining 61 (35 %) were negative. The classification performance was measured through tenfold cross-validation, root-mean-square error, sensitivity, and specificity. With an accuracy of 100 %, RMSE of 0, sensitivity of 100 %, and specificity of 100 %, the proposed method was able to successfully classify tuberculosis cases. In addition, the proposed method is comparable with top classifiers used in this research.
    Matched MeSH terms: Artificial Intelligence*
  9. Fulltext Use of the Griffiths Mental Scales in normal 2 year old Malaysian children
    Ho JJ, Amar HSS, Ismail R
    Med J Malaysia, 2001 Sep;56(3):331-5.
    PMID: 11732079
    The Griffiths Scales for Mental Development were used to assess a group of 60 normal 2-year old Malaysian children (25 Indian, 23 Malay and 12 Chinese). The mean GQ was 104.2 (SD 9.3). This was significantly higher than the test mean of 100, p < 0.001. The mean score for Malaysian children was significantly higher on the locomotor, personal social, performance and practical reasoning subscales while they were significantly lower on the hand eye subscale and did not differ from the test mean on the hearing and speech subscale. There was a significant correlation between GQ and social class, r = -0.39, p < 0.05. Scores were lower than those currently obtained on British children, p < 0.001. Minor difficulties due to language and cultural factors arose over the interpretation of several items but with standardisation of these items the test is useful in Malaysian children.
    Matched MeSH terms: Intelligence Tests*
  10. Health expert's tacit knowledge acquisition and representation using specialised healthcare scenarios
    Cheah YN, Abidi SS
    Stud Health Technol Inform, 2000;77:837-41.
    PMID: 11187672
    The abundance and transient nature to healthcare knowledge has rendered it difficult to acquire with traditional knowledge acquisition methods. In this paper, we propose a Knowledge Management approach, through the use of scenarios, as a mean to acquire and represent tacit healthcare knowledge. This proposition is based on the premise that tacit knowledge is best manifested in atypical situations. We also provide an overview of the representational scheme and novel acquisition mechanism of scenarios.
    Matched MeSH terms: Artificial Intelligence*
  11. Fulltext Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence
    Campero-Jurado I, Márquez-Sánchez S, Quintanar-Gómez J, Rodríguez S, Corchado JM
    Sensors (Basel), 2020 Nov 01;20(21).
    PMID: 33139608 DOI: 10.3390/s20216241
    Information and communication technologies (ICTs) have contributed to advances in Occupational Health and Safety, improving the security of workers. The use of Personal Protective Equipment (PPE) based on ICTs reduces the risk of accidents in the workplace, thanks to the capacity of the equipment to make decisions on the basis of environmental factors. Paradigms such as the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) make it possible to generate PPE models feasibly and create devices with more advanced characteristics such as monitoring, sensing the environment and risk detection between others. The working environment is monitored continuously by these models and they notify the employees and their supervisors of any anomalies and threats. This paper presents a smart helmet prototype that monitors the conditions in the workers' environment and performs a near real-time evaluation of risks. The data collected by sensors is sent to an AI-driven platform for analysis. The training dataset consisted of 11,755 samples and 12 different scenarios. As part of this research, a comparative study of the state-of-the-art models of supervised learning is carried out. Moreover, the use of a Deep Convolutional Neural Network (ConvNet/CNN) is proposed for the detection of possible occupational risks. The data are processed to make them suitable for the CNN and the results are compared against a Static Neural Network (NN), Naive Bayes Classifier (NB) and Support Vector Machine (SVM), where the CNN had an accuracy of 92.05% in cross-validation.
    Matched MeSH terms: Artificial Intelligence*
  12. Fulltext Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare-A Review
    Hasnul MA, Aziz NAA, Alelyani S, Mohana M, Aziz AA
    Sensors (Basel), 2021 Jul 23;21(15).
    PMID: 34372252 DOI: 10.3390/s21155015
    Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that are stressed, anguished, or depressed. Emotion recognition systems are an important technology that enables affective computing. Currently, there are a lot of ways to build an emotion recognition system using various techniques and algorithms. This review paper focuses on emotion recognition research that adopted electrocardiograms (ECGs) as a unimodal approach as well as part of a multimodal approach for emotion recognition systems. Critical observations of data collection, pre-processing, feature extraction, feature selection and dimensionality reduction, classification, and validation are conducted. This paper also highlights the architectures with accuracy of above 90%. The available ECG-inclusive affective databases are also reviewed, and a popularity analysis is presented. Additionally, the benefit of emotion recognition systems towards healthcare systems is also reviewed here. Based on the literature reviewed, a thorough discussion on the subject matter and future works is suggested and concluded. The findings presented here are beneficial for prospective researchers to look into the summary of previous works conducted in the field of ECG-based emotion recognition systems, and for identifying gaps in the area, as well as in developing and designing future applications of emotion recognition systems, especially in improving healthcare.
    Matched MeSH terms: Artificial Intelligence*
  13. Artificial intelligence and soft skills in radiation oncology: Data versus wisdom
    Boon IS, Lim JS, Yap MH, Au Yong TPT, Boon CS
    J Med Imaging Radiat Sci, 2020 12;51(4S):S114-S115.
    PMID: 32859543 DOI: 10.1016/j.jmir.2020.08.011
    Matched MeSH terms: Artificial Intelligence*
  14. Predicting the sorption efficiency of heavy metal based on the biochar characteristics, metal sources, and environmental conditions using various novel hybrid machine learning models
    Ke B, Nguyen H, Bui XN, Bui HB, Choi Y, Zhou J, et al.
    Chemosphere, 2021 Aug;276:130204.
    PMID: 34088091 DOI: 10.1016/j.chemosphere.2021.130204
    Heavy metals in water and wastewater are taken into account as one of the most hazardous environmental issues that significantly impact human health. The use of biochar systems with different materials helped significantly remove heavy metals in the water, especially wastewater treatment systems. Nevertheless, heavy metal's sorption efficiency on the biochar systems is highly dependent on the biochar characteristics, metal sources, and environmental conditions. Therefore, this study implicates the feasibility of biochar systems in the heavy metal sorption in water/wastewater and the use of artificial intelligence (AI) models in investigating efficiency sorption of heavy metal on biochar. Accordingly, this work investigated and proposed 20 artificial intelligent models for forecasting the sorption efficiency of heavy metal onto biochar based on five machine learning algorithms and bagging technique (BA). Accordingly, support vector machine (SVM), random forest (RF), artificial neural network (ANN), M5Tree, and Gaussian process (GP) algorithms were used as the key algorithms for the aim of this study. Subsequently, the individual models were bagged with each other to generate new ensemble models. Finally, 20 intelligent models were developed and evaluated, including SVM, RF, M5Tree, GP, ANN, BA-SVM, BA-RF, BA-M5Tree, BA-GP, BA-ANN, SVM-RF, SVM-M5Tree, SVM-GP, SVM-ANN, RF-M5Tree, RF-GP, RF-ANN, M5Tree-GP, M5Tree-ANN, GP-ANN. Of those, the hybrid models (i.e., BA-SVM, BA-RF, BA-M5Tree, BA-GP, BA-ANN, SVM-RF, SVM-M5Tree, SVM-GP, SVM-ANN, RF-M5Tree, RF-GP, RF-ANN, M5Tree-GP, M5Tree-ANN, GP-ANN) are introduced as the novelty of this study for estimating the heavy metal's sorption efficiency on the biochar systems. Also, the biochar characteristics, metal sources, and environmental conditions were comprehensively assessed and used, and they are considered as a novelty of the study as well. For this aim, a dataset of sorption efficiency of heavy metal was collected and processed with 353 experimental tests. Various performance indexes were applied to evaluate the models, such as RMSE, R2, MAE, color intensity, Taylor diagram, box and whiskers plots. This study's findings revealed that AI models could predict heavy metal's sorption efficiency onto biochar with high reliability, and the efficiency of the ensemble models is higher than those of individual models. The results also reported that the SVM-ANN ensemble model is the most superior model among 20 developed models. The predictive model proposed that heavy metal's efficiency sorption on biochar can be accurately forecasted and early warning for the water pollution by heavy metal.
    Matched MeSH terms: Artificial Intelligence*
  15. Fulltext Faking good in personality and emotional intelligent tests: Self-enhancement among a cohort of medical school applicants
    Muhamad Saiful Bahri Yusoff
    Education in Medicine Journal, 2013;5(2):60-71.
    MyJurnal
    Introduction: There is lacking of evidence available in literatures on faking good in personality and emotional intelligent (EI) tests among medical school applicants. Thus more research is required to address the faking good issues in medical context specifically related to student admission. Objective: This study aimed to estimate the prevalence of faking good in personality and EI tests during a high stake situation which was during student selection process. Method: A one-year prospective study was carried out on a cohort of medical school applicants. Data collection was carried out at five different intervals; one measurement at pre-selection (Time 1) and four measurements at post-selection (Time 2 to Time 5). The USMaP-i and USMEQ-i were used to measure personality and EI. Faking good was positive if the scores at Time 1 beyond the 95% CI of average scores of Time 2 to Time 5.Result: The highest prevalence of faking good among personality traits was in conscientiousness (83.1%) and the lowest was in openness (74.1%). The highest prevalence of faking good among EI constructs was in emotional conscientiousness (77%) and the lowest was in emotional awareness (51.7%). About 1.7% of applicants were not faking good at any of the personality dimensions while 11.5% of them were not faking good at any of the EI constructs. About 47.4% faked good at all the personality dimensions and 33.9% faked good at all the EI constructs. Conclusion: The prevalence of faking good in the self-reporting personality and EI tests was high. Certain personality traits and EI constructs were more susceptible to faking good. The personality test was more susceptible to faking good than the EI test. Considering the potential positive impacts of personality and EI on individual performance, alternative ways should be designed to address the faking good issues.
    Matched MeSH terms: Intelligence Tests; Emotional Intelligence
  16. A Systematic Review for Human EEG Brain Signals Based Emotion Classification, Feature Extraction, Brain Condition, Group Comparison
    Hamada M, Zaidan BB, Zaidan AA
    J Med Syst, 2018 Jul 24;42(9):162.
    PMID: 30043178 DOI: 10.1007/s10916-018-1020-8
    The study of electroencephalography (EEG) signals is not a new topic. However, the analysis of human emotions upon exposure to music considered as important direction. Although distributed in various academic databases, research on this concept is limited. To extend research in this area, the researchers explored and analysed the academic articles published within the mentioned scope. Thus, in this paper a systematic review is carried out to map and draw the research scenery for EEG human emotion into a taxonomy. Systematically searched all articles about the, EEG human emotion based music in three main databases: ScienceDirect, Web of Science and IEEE Xplore from 1999 to 2016. These databases feature academic studies that used EEG to measure brain signals, with a focus on the effects of music on human emotions. The screening and filtering of articles were performed in three iterations. In the first iteration, duplicate articles were excluded. In the second iteration, the articles were filtered according to their titles and abstracts, and articles outside of the scope of our domain were excluded. In the third iteration, the articles were filtered by reading the full text and excluding articles outside of the scope of our domain and which do not meet our criteria. Based on inclusion and exclusion criteria, 100 articles were selected and separated into five classes. The first class includes 39 articles (39%) consists of emotion, wherein various emotions are classified using artificial intelligence (AI). The second class includes 21 articles (21%) is composed of studies that use EEG techniques. This class is named 'brain condition'. The third class includes eight articles (8%) is related to feature extraction, which is a step before emotion classification. That this process makes use of classifiers should be noted. However, these articles are not listed under the first class because these eight articles focus on feature extraction rather than classifier accuracy. The fourth class includes 26 articles (26%) comprises studies that compare between or among two or more groups to identify and discover human emotion-based EEG. The final class includes six articles (6%) represents articles that study music as a stimulus and its impact on brain signals. Then, discussed the five main categories which are action types, age of the participants, and number size of the participants, duration of recording and listening to music and lastly countries or authors' nationality that published these previous studies. it afterward recognizes the main characteristics of this promising area of science in: motivation of using EEG process for measuring human brain signals, open challenges obstructing employment and recommendations to improve the utilization of EEG process.
    Matched MeSH terms: Artificial Intelligence*
  17. Abstracts of the 2nd Biennial Medical & Health Sciences Conference, Universiti Tunku Abdul Rahman 2019
    Malays J Pathol, 2019 Dec;41(3):431-457.
    PMID: 31901928
    No abstract available.
    Matched MeSH terms: Artificial Intelligence*
  18. Fulltext An artificial intelligence model correlated with morphological and genetic features of blastocyst quality improves ranking of viable embryos
    Diakiw SM, Hall JMM, VerMilyea M, Lim AYX, Quangkananurug W, Chanchamroen S, et al.
    Reprod Biomed Online, 2022 Dec;45(6):1105-1117.
    PMID: 36117079 DOI: 10.1016/j.rbmo.2022.07.018
    RESEARCH QUESTION: Can better methods be developed to evaluate the performance and characteristics of an artificial intelligence model for evaluating the likelihood of clinical pregnancy based on analysis of day-5 blastocyst-stage embryos, such that performance evaluation more closely reflects clinical use in IVF procedures, and correlations with known features of embryo quality are identified?

    DESIGN: De-identified images were provided retrospectively or collected prospectively by IVF clinics using the artificial intelligence model in clinical practice. A total of 9359 images were provided by 18 IVF clinics across six countries, from 4709 women who underwent IVF between 2011 and 2021. Main outcome measures included clinical pregnancy outcome (fetal heartbeat at first ultrasound scan), embryo morphology score, and/or pre-implantation genetic testing for aneuploidy (PGT-A) results.

    RESULTS: A positive linear correlation of artificial intelligence scores with pregnancy outcomes was found, and up to a 12.2% reduction in time to pregnancy (TTP) was observed when comparing the artificial intelligence model with standard morphological grading methods using a novel simulated cohort ranking method. Artificial intelligence scores were significantly correlated with known morphological features of embryo quality based on the Gardner score, and with previously unknown morphological features associated with embryo ploidy status, including chromosomal abnormalities indicative of severity when considering embryos for transfer during IVF.

    CONCLUSION: Improved methods for evaluating artificial intelligence for embryo selection were developed, and advantages of the artificial intelligence model over current grading approaches were highlighted, strongly supporting the use of the artificial intelligence model in a clinical setting.

    Matched MeSH terms: Artificial Intelligence*
  19. Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia
    Jumin E, Basaruddin FB, Yusoff YBM, Latif SD, Ahmed AN
    Environ Sci Pollut Res Int, 2021 Jun;28(21):26571-26583.
    PMID: 33484461 DOI: 10.1007/s11356-021-12435-6
    Reliable and accurate prediction model capturing the changes in solar radiation is essential in the power generation and renewable carbon-free energy industry. Malaysia has immense potential to develop such an industry due to its location in the equatorial zone and its climatic characteristics with high solar energy resources. However, solar energy accounts for only 2-4.6% of total energy utilization. Recently, in developed countries, various prediction models based on artificial intelligence (AI) techniques have been applied to predict solar radiation. In this study, one of the most recent AI algorithms, namely, boosted decision tree regression (BDTR) model, was applied to predict the changes in solar radiation based on collected data in Malaysia. The proposed model then compared with other conventional regression algorithms, such as linear regression and neural network. Two different normalization techniques (Gaussian normalizer binning normalizer), splitting size, and different input parameters were investigated to enhance the accuracy of the models. Sensitivity analysis and uncertainty analysis were introduced to validate the accuracy of the proposed model. The results revealed that BDTR outperformed other algorithms with a high level of accuracy. The funding of this study could be used as a reliable tool by engineers to improve the renewable energy sector in Malaysia and provide alternative sustainable energy resources.
    Matched MeSH terms: Artificial Intelligence*
  20. Revolutionising dental technologies: a qualitative study on dental technicians' perceptions of Artificial intelligence integration
    Lin GSS, Ng YS, Ghani NRNA, Chua KH
    BMC Oral Health, 2023 Sep 25;23(1):690.
    PMID: 37749537 DOI: 10.1186/s12903-023-03389-x
    BACKGROUND: The integration of artificial intelligence (AI) in dentistry has the potential to revolutionise the field of dental technologies. However, dental technicians' views on the use of AI in dental technology are still sparse in the literature. This qualitative study aimed to explore the perceptions of dental technicians regarding the use of AI in their dental laboratory practice.

    METHODS: Twelve dental technicians with at least five years of professional experience and currently working in Malaysia agreed to participate in the one-to-one in-depth online interviews. Interviews were recorded, transcribed verbatim and translated. Thematic analysis was conducted to identify patterns, themes, and categories within the interview transcripts.

    RESULTS: The analysis revealed two key themes: "Perceived Benefits of AI" and "Concerns and Challenges". Dental technicians recognised the enhanced efficiency, productivity, accuracy, and precision that AI can bring to dental laboratories. They also acknowledged the streamlined workflow and improved communication facilitated by AI systems. However, concerns were raised regarding job security, professional identity, ethical considerations, and the need for adequate training and support.

    CONCLUSION: This research sheds light on the potential benefits and challenges associated with the integration of AI in dental laboratory practices. Understanding these perceptions and addressing the challenges can support the effective integration of AI in dental laboratories and contribute to the growing body of literature on AI in healthcare.

    Matched MeSH terms: Artificial Intelligence*
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