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Fulltext The optimization and impact of public sports service quality based on the supervised learning model and artificial intelligence

Yan Y

Sci Rep, 2025 Mar 22;15(1):9923.
PMID: 40121333 DOI: 10.1038/s41598-025-94613-x

Aiming at the optimization of public sports service quality, this study analyzes the public sports service data deeply by constructing a supervised learning model. Firstly, the theoretical framework of this study is established. Secondly, the technical framework is constructed based on the supervised learning model. Finally, the comprehensive performance of the model is evaluated using a dataset and practical application. The results show that when the model is used to process public sports service data, its performance is excellent. Specifically, the model's accuracy and recall in processing various types of data markedly exceed expectations, with the accuracy reaching more than 88% and the recall remaining at a similarly high level. This remarkable result not only validates the supervised learning model's practicability in the quality optimization of public sports services but also highlights its huge application potential and value. In addition, the possibility and challenge of the model in practical application are also discussed, which provides a useful reference for further improving the quality of public sports service. The findings of this study enrich the research methods in the field of public sports services and offer a scientific basis for relevant decision-making, which helps promote the continuous optimization and development of public sports services.

Matched MeSH terms: Artificial Intelligence*
Intelligent skin disease prediction system using transfer learning and explainable artificial intelligence

Abbas S, Ahmed F, Khan WA, Ahmad M, Khan MA, Ghazal TM

Sci Rep, 2025 Jan 11;15(1):1746.
PMID: 39799199 DOI: 10.1038/s41598-024-83966-4

Skin diseases impact millions of people around the world and pose a severe risk to public health. These diseases have a wide range of effects on the skin's structure, functionality, and appearance. Identifying and predicting skin diseases are laborious processes that require a complete physical examination, a review of the patient's medical history, and proper laboratory diagnostic testing. Additionally, it necessitates a significant number of histological and clinical characteristics for examination and subsequent treatment. As a disease's complexity and quantity of features grow, identifying and predicting it becomes more challenging. This research proposes a deep learning (DL) model utilizing transfer learning (TL) to quickly identify skin diseases like chickenpox, measles, and monkeypox. A pre-trained VGG16 is used for transfer learning. The VGG16 can identify and predict diseases more quickly by learning symptom patterns. Images of the skin from the four classes of chickenpox, measles, monkeypox, and normal are included in the dataset. The dataset is separated into training and testing. The experimental results performed on the dataset demonstrate that the VGG16 model can identify and predict skin diseases with 93.29% testing accuracy. However, the VGG16 model does not explain why and how the system operates because deep learning models are black boxes. Deep learning models' opacity stands in the way of their widespread application in the healthcare sector. In order to make this a valuable system for the health sector, this article employs layer-wise relevance propagation (LRP) to determine the relevance scores of each input. The identified symptoms provide valuable insights that could support timely diagnosis and treatment decisions for skin diseases.

Matched MeSH terms: Artificial Intelligence*
Artificial Intelligence in Tuberculosis Imaging: A Global Bibliometric Analysis of Research Trends and Collaborations

Wei XB, Mohd Norsuddin N, Azmi MI, Hamid HA

Radiography (Lond), 2026 Apr;32(3):103348.
PMID: 41687417 DOI: 10.1016/j.radi.2026.103348

INTRODUCTION: Tuberculosis (TB), a leading infectious cause of death, remains a global health challenge. Imaging is central to diagnosis and screening, while artificial intelligence (AI) is increasingly applied to chest X-rays (CXR) and computed tomography (CT). However, no bibliometric study has comprehensively mapped publication trends, collaborations, modalities, technological evolution, and emerging research hotspots in AI-driven TB imaging.
METHODS: Publications on AI-based TB imaging were retrieved from Web of Science (WoS) and Scopus (2000 to July 2025), and a bibliometric analysis was performed on English-language articles and reviews. CiteSpace and VOSviewer were used to analyze publication trends, international and institutional collaborations, imaging modalities, keyword co-occurrence networks, clustering, and influential papers.
RESULTS: A total of 556 publications were identified. Annual publication output increased rapidly, with a compound annual growth rate of 21.2 % from 2000 to 2024. India and China led in output. CXR was the dominant imaging modality. Technological progress followed three phases: segmentation-based computer-aided diagnosis (CAD) systems, the deep learning revolution, and clinical deployment of AI systems. Keyword co-occurrence clustering identified major research domains, including "machine learning," "computer-aided detection," and "tuberculosis detection." Influential studies demonstrate a clear shift from algorithm development to large-scale screening validation and clinical use.
CONCLUSION: This bibliometric study reveals that AI in TB imaging has expanded rapidly, progressing from basic early CAD to clinically relevant deep learning-driven applications. CXR remains essential for large-scale screening. Future efforts emphasize interpretable and generalizable AI solutions to maximize global impact on TB diagnosis and control.
IMPLICATIONS FOR PRACTICE: The findings highlight the value of integrating AI-based imaging into clinical and public health practice. With international collaboration and technical support, CXR-based AI can facilitate TB detection in high-burden regions.

Matched MeSH terms: Artificial Intelligence*
Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

Gautam V, Gaurav A, Masand N, Lee VS, Patil VM

Mol Divers, 2023 Apr;27(2):959-985.
PMID: 35819579 DOI: 10.1007/s11030-022-10489-3

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.

Matched MeSH terms: Artificial Intelligence*
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*
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*
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*
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*
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*
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*
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*
The assessment of emerging data-intelligence technologies for modeling Mg+2 and SO4-2 surface water quality

Jamei M, Ahmadianfar I, Karbasi M, Jawad AH, Farooque AA, Yaseen ZM

J Environ Manage, 2021 Dec 15;300:113774.
PMID: 34560461 DOI: 10.1016/j.jenvman.2021.113774

The concentration of soluble salts in surface water and rivers such as sodium, sulfate, chloride, magnesium ions, etc., plays an important role in the water salinity. Therefore, accurate determination of the distribution pattern of these ions can improve better management of drinking water resources and human health. The main goal of this research is to establish two novel wavelet-complementary intelligence paradigms so-called wavelet least square support vector machine coupled with improved simulated annealing (W-LSSVM-ISA) and the wavelet extended Kalman filter integrated with artificial neural network (W-EKF- ANN) for accurate forecasting of the monthly), magnesium (Mg+2), and sulfate (SO4-2) indices at Maroon River, in Southwest of Iran. The monthly River flow (Q), electrical conductivity (EC), Mg+2, and SO4-2 data recorded at Tange-Takab station for the period 1980-2016. Some preprocessing procedures consisting of specifying the number of lag times and decomposition of the existing original signals into multi-resolution sub-series using three mother wavelets were performed to develop predictive models. In addition, the best subset regression analysis was designed to separately assess the best selective combinations for Mg+2 and SO4-2. The statistical metrics and authoritative validation approaches showed that both complementary paradigms yielded promising accuracy compared with standalone artificial intelligence (AI) models. Furthermore, the results demonstrated that W-LSSVM-ISA-C1 (correlation coefficient (R) = 0.9521, root mean square error (RMSE) = 0.2637 mg/l, and Kling-Gupta efficiency (KGE) = 0.9361) and W-LSSVM-ISA-C4 (R = 0.9673, RMSE = 0.5534 mg/l and KGE = 0.9437), using Dmey mother that outperformed the W-EKF-ANN for predicting Mg+2 and SO4-2, respectively.

Matched MeSH terms: Artificial Intelligence*
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*
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*
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*
Fulltext Enhancing business intelligence by means of suggestive reviews

Qazi A, Raj RG, Tahir M, Cambria E, Syed KB

ScientificWorldJournal, 2014;2014:879323.
PMID: 25054188 DOI: 10.1155/2014/879323

Appropriate identification and classification of online reviews to satisfy the needs of current and potential users pose a critical challenge for the business environment. This paper focuses on a specific kind of reviews: the suggestive type. Suggestions have a significant influence on both consumers' choices and designers' understanding and, hence, they are key for tasks such as brand positioning and social media marketing. The proposed approach consists of three main steps: (1) classify comparative and suggestive sentences; (2) categorize suggestive sentences into different types, either explicit or implicit locutions; (3) perform sentiment analysis on the classified reviews. A range of supervised machine learning approaches and feature sets are evaluated to tackle the problem of suggestive opinion mining. Experimental results for all three tasks are obtained on a dataset of mobile phone reviews and demonstrate that extending a bag-of-words representation with suggestive and comparative patterns is ideal for distinguishing suggestive sentences. In particular, it is observed that classifying suggestive sentences into implicit and explicit locutions works best when using a mixed sequential rule feature representation. Sentiment analysis achieves maximum performance when employing additional preprocessing in the form of negation handling and target masking, combined with sentiment lexicons.

Matched MeSH terms: Artificial Intelligence*
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*
Fulltext ARCHERY: a prospective observational study of artificial intelligence-based radiotherapy treatment planning for cervical, head and neck and prostate cancer - study protocol

Aggarwal A, Court LE, Hoskin P, Jacques I, Kroiss M, Laskar S, et al.

BMJ Open, 2023 Dec 07;13(12):e077253.
PMID: 38149419 DOI: 10.1136/bmjopen-2023-077253

INTRODUCTION: Fifty per cent of patients with cancer require radiotherapy during their disease course, however, only 10%-40% of patients in low-income and middle-income countries (LMICs) have access to it. A shortfall in specialised workforce has been identified as the most significant barrier to expanding radiotherapy capacity. Artificial intelligence (AI)-based software has been developed to automate both the delineation of anatomical target structures and the definition of the position, size and shape of the radiation beams. Proposed advantages include improved treatment accuracy, as well as a reduction in the time (from weeks to minutes) and human resources needed to deliver radiotherapy.
METHODS: ARCHERY is a non-randomised prospective study to evaluate the quality and economic impact of AI-based automated radiotherapy treatment planning for cervical, head and neck, and prostate cancers, which are endemic in LMICs, and for which radiotherapy is the primary curative treatment modality. The sample size of 990 patients (330 for each cancer type) has been calculated based on an estimated 95% treatment plan acceptability rate. Time and cost savings will be analysed as secondary outcome measures using the time-driven activity-based costing model. The 48-month study will take place in six public sector cancer hospitals in India (n=2), Jordan (n=1), Malaysia (n=1) and South Africa (n=2) to support implementation of the software in LMICs.
ETHICS AND DISSEMINATION: The study has received ethical approval from University College London (UCL) and each of the six study sites. If the study objectives are met, the AI-based software will be offered as a not-for-profit web service to public sector state hospitals in LMICs to support expansion of high quality radiotherapy capacity, improving access to and affordability of this key modality of cancer cure and control. Public and policy engagement plans will involve patients as key partners.

Matched MeSH terms: Artificial Intelligence*
Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors

Shiammala PN, Duraimutharasan NKB, Vaseeharan B, Alothaim AS, Al-Malki ES, Snekaa B, et al.

Methods, 2023 Nov;219:82-94.
PMID: 37778659 DOI: 10.1016/j.ymeth.2023.09.010

Artificial intelligence (AI), particularly deep learning as a subcategory of AI, provides opportunities to accelerate and improve the process of discovering and developing new drugs. The use of AI in drug discovery is still in its early stages, but it has the potential to revolutionize the way new drugs are discovered and developed. As AI technology continues to evolve, it is likely that AI will play an even greater role in the future of drug discovery. AI is used to identify new drug targets, design new molecules, and predict the efficacy and safety of potential drugs. The inclusion of AI in drug discovery can screen millions of compounds in a matter of hours, identifying potential drug candidates that would have taken years to find using traditional methods. AI is highly utilized in the pharmaceutical industry by optimizing processes, reducing waste, and ensuring quality control. This review covers much-needed topics, including the different types of machine-learning techniques, their applications in drug discovery, and the challenges and limitations of using machine learning in this field. The state-of-the-art of AI-assisted pharmaceutical discovery is described, covering applications in structure and ligand-based virtual screening, de novo drug creation, prediction of physicochemical and pharmacokinetic properties, drug repurposing, and related topics. Finally, many obstacles and limits of present approaches are outlined, with an eye on potential future avenues for AI-assisted drug discovery and design.

Matched MeSH terms: Artificial Intelligence*
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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