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Fulltext What's been trending with OSCEs in pharmacy education over the last 20 years? A bibliometric review and content analysis

Lim AS, Ling YL, Wilby KJ, Mak V

Curr Pharm Teach Learn, 2024 Mar;16(3):212-220.
PMID: 38171979 DOI: 10.1016/j.cptl.2023.12.028

BACKGROUND: Objective structured clinical examinations (OSCEs) remain an integral part of pharmacy education. This study aimed to characterize key researchers, areas, and themes in pharmacy education OSCEs using a bibliometric review with content analysis.
METHODS: A bibliometric review was conducted on literature from over 23 years from January 2000 to May 2023. Articles focusing on any type of OSCE research in pharmacy education in both undergraduate and postgraduate sectors were included. Articles were excluded if they were not original articles or not published in English. A summative content analysis was also conducted to identify key topics.
RESULTS: A total of 192 articles were included in the analysis. There were 242 institutions that contributed to the OSCE literature in pharmacy education, with the leading country being Canada. Most OSCE research came from developed countries and were descriptive studies based on single institution data. The top themes emerging from content analysis were student perceptions on OSCE station styles (n = 98), staff perception (n = 19), grade assessment of OSCEs (n = 145), interprofessional education (n = 11), standardized patients (n = 12), and rubric development and standard setting (n = 8).
IMPLICATIONS: There has been a growth in virtual OSCEs, interprofessional OSCEs, and artificial intelligence OSCEs. Communication rubrics and minimizing assessor variability are still trending research areas. There is scope to conduct more research on evaluating specific types of OSCEs, when best to hold an OSCE, and comparing OSCEs to other assessments.

Matched MeSH terms: Artificial Intelligence*
What does ChatGPT advise about predatory publishing?

Tsigaris P, Kendall G, Teixeira da Silva JA

J Prof Nurs, 2023;49:188-189.
PMID: 38042556 DOI: 10.1016/j.profnurs.2023.08.002

The debate surrounding "predatory publishing" continues to be unable to find entirely effective solutions to dealing with this problem, despite fervent efforts by many academics and policy makers around the world. Given this situation, we were interested in appreciating whether ChatGPT would be able to offer insight and solutions, to complement current human-based efforts.

Matched MeSH terms: Artificial Intelligence*
Weighted voting-based consensus clustering for chemical structure databases

Saeed F, Ahmed A, Shamsir MS, Salim N

J Comput Aided Mol Des, 2014 Jun;28(6):675-84.
PMID: 24830925 DOI: 10.1007/s10822-014-9750-2

The cluster-based compound selection is used in the lead identification process of drug discovery and design. Many clustering methods have been used for chemical databases, but there is no clustering method that can obtain the best results under all circumstances. However, little attention has been focused on the use of combination methods for chemical structure clustering, which is known as consensus clustering. Recently, consensus clustering has been used in many areas including bioinformatics, machine learning and information theory. This process can improve the robustness, stability, consistency and novelty of clustering. For chemical databases, different consensus clustering methods have been used including the co-association matrix-based, graph-based, hypergraph-based and voting-based methods. In this paper, a weighted cumulative voting-based aggregation algorithm (W-CVAA) was developed. The MDL Drug Data Report (MDDR) benchmark chemical dataset was used in the experiments and represented by the AlogP and ECPF_4 descriptors. The results from the clustering methods were evaluated by the ability of the clustering to separate biologically active molecules in each cluster from inactive ones using different criteria, and the effectiveness of the consensus clustering was compared to that of Ward's method, which is the current standard clustering method in chemoinformatics. This study indicated that weighted voting-based consensus clustering can overcome the limitations of the existing voting-based methods and improve the effectiveness of combining multiple clusterings of chemical structures.

Matched MeSH terms: Artificial Intelligence
Water treatment and artificial intelligence techniques: a systematic literature review research

Ismail W, Niknejad N, Bahari M, Hendradi R, Zaizi NJM, Zulkifli MZ

Environ Sci Pollut Res Int, 2023 Jun;30(28):71794-71812.
PMID: 34609681 DOI: 10.1007/s11356-021-16471-0

As clean water can be considered among the essentials of human life, there is always a requirement to seek its foremost and high quality. Water primarily becomes polluted due to organic as well as inorganic pollutants, including nutrients, heavy metals, and constant contamination with organic materials. Predicting the quality of water accurately is essential for its better management along with controlling pollution. With stricter laws regarding water treatment to remove organic and biologic materials along with different pollutants, looking for novel technologic procedures will be necessary for improved control of the treatment processes by water utilities. Linear regression-based models with relative simplicity considering water prediction have been typically used as available statistical models. Nevertheless, in a majority of real problems, particularly those associated with modeling of water quality, non-linear patterns will be observed, requiring non-linear models to address them. Thus, artificial intelligence (AI) can be a good candidate in modeling and optimizing the elimination of pollutants from water in empirical settings with the ability to generate ideal operational variables, due to its recent considerable advancements. Management and operation of water treatment procedures are supported technically by these technologies, leading to higher efficiency compared to sole dependence on human operations. Thus, establishing predictive models for water quality and subsequently, more efficient management of water resources would be critically important, serving as a strong tool. A systematic review methodology has been employed in the present work to investigate the previous studies over the time interval of 2010-2020, while analyzing and synthesizing the literature, particularly regarding AI application in water treatment. A total number of 92 articles had addressed the topic under study using AI. Based on the conclusions, the application of AI can obviously facilitate operations, process automation, and management of water resources in significantly volatile contexts.

Matched MeSH terms: Artificial Intelligence
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*
Fulltext Usable Mobile App for Community Education on Colorectal Cancer: Development Process and Usability Study

Mohamad Marzuki MF, Yaacob NA, Bin Yaacob NM, Abu Hassan MR, Ahmad SB

JMIR Hum Factors, 2019 Apr 16;6(2):e12103.
PMID: 30990454 DOI: 10.2196/12103

BACKGROUND: Participation in colorectal cancer screening is still low among Malaysians despite the increasing trend of incidence, with more than half of the new cases being detected in the advanced stages. Knowledge improvement might increase screening participation and thus improve the chances of disease detection. With the advancement of communication technology, people nowadays prefer to read from their mobile phone using a Web browser or mobile apps compared with the traditional printed material. Therefore, health education and promotion should adapt this behavior change in educating the community.
OBJECTIVE: This study aimed to document the process of designing and developing a mobile app for community education on colorectal cancer and assess the usability of the prototype.
METHODS: The nominal group technique (NGT) was used for the content development of the mobile app. NGT involving community educationists and clinicians combined with community representatives as the target users identified relevant health information and communication strategies including features for a user-friendly mobile app. The prototype was developed using framework Ionic 1, based on the Apache Cordova and Angular JS (Google). It was published in the Google Play store. In total, 50 mobile phone users aged 50 years and above and who had never been diagnosed with any type of cancer were invited to download and use the app. They were asked to assess the usability of the app using the validated Malay version of System Usability Scale Questionnaire for the Assessment of Mobile Apps questionnaire. The One-sample t test was used to assess the usability score with a cut-off value of 68 for the usable mobile app.
RESULTS: The Colorectal Cancer Awareness Application (ColorApp) was successfully developed in the local Malay language. The NGT discussion had suggested 6 main menus in the ColorApp prototype, which are Introduction, Sign and Symptoms, Risk Factors, Preventive Measures, Colorectal Cancer Screening Program, and immunochemical fecal occult blood test kit. A total of 2 additional artificial intelligence properties menus were added to allow user-ColorApp interaction: Analyze Your Status and ColorApp Calculator. The prototype has been published in the Google Play store. The mean usability score was 72 (SD 11.52), which indicates that ColorApp is a usable mobile app, and it can be used as a tool for community education on colorectal cancer.
CONCLUSIONS: ColorApp mobile app can be used as a user-friendly tool for community education on colorectal cancer.

Matched MeSH terms: Artificial Intelligence
Fulltext Tuberculosis disease diagnosis using artificial immune recognition system

Shamshirband S, Hessam S, Javidnia H, Amiribesheli M, Vahdat S, Petković D, et al.

Int J Med Sci, 2014;11(5):508-14.
PMID: 24688316 DOI: 10.7150/ijms.8249

There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods.

Matched MeSH terms: Artificial Intelligence
Transforming XML-based electronic patient records for use in medical case based reasoning systems

Abidi SS, Manickam S

Stud Health Technol Inform, 2000;77:709-13.
PMID: 11187645

Electronic patient records (EPR) can be regarded as an implicit source of clinical behaviour and problem-solving knowledge, systematically compiled by clinicians. We present an approach, together with its computational implementation, to pro-actively transform XML-based EPR into specialised Clinical Cases (CC) in the realm of Medical Case Base Systems. The 'correct' transformation of EPR to CC involves structural, terminological and conceptual standardisation, which is achieved by a confluence of techniques and resources, such as XML, UMLS (meta-thesaurus) and medical knowledge ontologies. We present below the functional architecture of a Medical Case-Base Reasoning Info-Structure (MCRIS) that features two distinct, yet related, functionalities: (1) a generic medical case-based reasoning system for decision-support activities; and (2) an EPR-CC transformation system to transform typical EPR's to CC.

Matched MeSH terms: Artificial Intelligence*
Fulltext Towards hospital-on-chip supported by 2D MXenes-based 5th generation intelligent biosensors

Chaudhary V, Khanna V, Ahmed Awan HT, Singh K, Khalid M, Mishra YK, et al.

Biosens Bioelectron, 2023 Jan 15;220:114847.
PMID: 36335709 DOI: 10.1016/j.bios.2022.114847

Existing public health emergencies due to fatal/infectious diseases such as coronavirus disease (COVID-19) and monkeypox have raised the paradigm of 5th generation portable intelligent and multifunctional biosensors embedded on a single chip. The state-of-the-art 5th generation biosensors are concerned with integrating advanced functional materials with controllable physicochemical attributes and optimal machine processability. In this direction, 2D metal carbides and nitrides (MXenes), owing to their enhanced effective surface area, tunable physicochemical properties, and rich surface functionalities, have shown promising performances in biosensing flatlands. Moreover, their hybridization with diversified nanomaterials caters to their associated challenges for the commercialization of stability due to restacking and oxidation. MXenes and its hybrid biosensors have demonstrated intelligent and lab-on-chip prospects for determining diverse biomarkers/pathogens related to fatal and infectious diseases. Recently, on-site detection has been clubbed with solution-on-chip MXenes by interfacing biosensors with modern-age technologies, including 5G communication, internet-of-medical-things (IoMT), artificial intelligence (AI), and data clouding to progress toward hospital-on-chip (HOC) modules. This review comprehensively summarizes the state-of-the-art MXene fabrication, advancements in physicochemical properties to architect biosensors, and the progress of MXene-based lab-on-chip biosensors toward HOC solutions. Besides, it discusses sustainable aspects, practical challenges and alternative solutions associated with these modules to develop personalized and remote healthcare solutions for every individual in the world.

Matched MeSH terms: Artificial Intelligence
Fulltext Towards Integrated Air Pollution Monitoring and Health Impact Assessment Using Federated Learning: A Systematic Review

Neo EX, Hasikin K, Mokhtar MI, Lai KW, Azizan MM, Razak SA, et al.

Front Public Health, 2022;10:851553.
PMID: 35664109 DOI: 10.3389/fpubh.2022.851553

Environmental issues such as environmental pollutions and climate change are the impacts of globalization and become debatable issues among academics and industry key players. One of the environmental issues which is air pollution has been catching attention among industrialists, researchers, and communities around the world. However, it has always neglected until the impacts on human health become worse, and at times, irreversible. Human exposure to air pollutant such as particulate matters, sulfur dioxide, ozone and carbon monoxide contributed to adverse health hazards which result in respiratory diseases, cardiorespiratory diseases, cancers, and worst, can lead to death. This has led to a spike increase of hospitalization and emergency department visits especially at areas with worse pollution cases that seriously impacting human life and health. To address this alarming issue, a predictive model of air pollution is crucial in assessing the impacts of health due to air pollution. It is also critical in predicting the air quality index when assessing the risk contributed by air pollutant exposure. Hence, this systemic review explores the existing studies on anticipating air quality impact to human health using the advancement of Artificial Intelligence (AI). From the extensive review, we highlighted research gaps in this field that are worth to inquire. Our study proposes to develop an AI-based integrated environmental and health impact assessment system using federated learning. This is specifically aims to identify the association of health impact and pollution based on socio-economic activities and predict the Air Quality Index (AQI) for impact assessment. The output of the system will be utilized for hospitals and healthcare services management and planning. The proposed solution is expected to accommodate the needs of the critical and prioritization of sensitive group of publics during pollution seasons. Our finding will bring positive impacts to the society in terms of improved healthcare services quality, environmental and health sustainability. The findings are beneficial to local authorities either in healthcare or environmental monitoring institutions especially in the developing countries.

Matched MeSH terms: Artificial Intelligence
Fulltext Top Mobile Applications in Pediatrics and Children's Health: Assessment and Intelligent Analysis Tools for a Systematic Investigation

Jazayeri SMHM, Jamshidnezhad A

Malays J Med Sci, 2019 Jan;26(1):5-14.
PMID: 30914890 DOI: 10.21315/mjms2019.26.1.2

The development of intelligent software in recent years has grown rapidly. Mobile health has become a field of interest as a tool for childcare, especially as a means for parents of children with diverse diseases and a resource to promote their health conditions. Current systematic review was conducted to survey the functionalities of available applications on the mobile platform to support pediatrics intelligent diagnosis and children healthcare. Results which met the inclusion criteria (such as patient monitoring, decision support, diagnosis support) were obtained, assessed and organised into a checklist. In this study, 379 potential apps were identified using the search feature in Apple App Store and Google Play Store. After careful consideration of the selected apps, only three (Google Play Store) and one (iTunes Store), fulfilled all the general inclusion criteria and special criteria, such as intelligence tools. The results showed that Artificial Intelligence (AI) was used minimally in diagnostic apps due to a limited amount of mobile hardware and software, such as the reliable programming of intelligent algorithms.

Matched MeSH terms: Artificial Intelligence
Fulltext Three-dimensional semi-automated volumetric assessment of the pulp space of teeth following regenerative dental procedures

Shetty H, Shetty S, Kakade A, Shetty A, Karobari MI, Pawar AM, et al.

Sci Rep, 2021 11 09;11(1):21914.
PMID: 34754049 DOI: 10.1038/s41598-021-01489-8

The volumetric change that occurs in the pulp space over time represents a critical measure when it comes to determining the secondary outcomes of regenerative endodontic procedures (REPs). However, to date, only a few studies have investigated the accuracy of the available domain-specialized medical imaging tools with regard to three-dimensional (3D) volumetric assessment. This study sought to compare the accuracy of two different artificial intelligence-based medical imaging programs namely OsiriX MD (v 9.0, Pixmeo SARL, Bernex Switzerland, https://www.osirix-viewer.com ) and 3D Slicer ( http://www.slicer.org ), in terms of estimating the volume of the pulp space following a REP. An Invitro assessment was performed to check the reliability and sensitivity of the two medical imaging programs in use. For the subsequent clinical application, pre- and post-procedure cone beam computed tomography scans of 35 immature permanent teeth with necrotic pulp and periradicular pathosis that had been treated with a cell-homing concept-based REP were processed using the two biomedical DICOM software programs (OsiriX MD and 3D Slicer). The volumetric changes in the teeth's pulp spaces were assessed using semi-automated techniques in both programs. The data were statistically analyzed using t-tests and paired t-tests (P = 0.05). The pulp space volumes measured using both programs revealed a statistically significant decrease in the pulp space volume following the REP (P 0.05). The mean decreases in the pulp space volumes measured using OsiriX MD and 3D Slicer were 25.06% ± 19.45% and 26.10% ± 18.90%, respectively. The open-source software (3D Slicer) was found to be as accurate as the commercially available software with regard to the volumetric assessment of the post-REP pulp space. This study was the first to demonstrate the step-by-step application of 3D Slicer, a user-friendly and easily accessible open-source multiplatform software program for the segmentation and volume estimation of the pulp spaces of teeth treated with REPs.

Matched MeSH terms: Artificial Intelligence
Fulltext Threats by artificial intelligence to human health and human existence

Federspiel F, Mitchell R, Asokan A, Umana C, McCoy D

BMJ Glob Health, 2023 May;8(5).
PMID: 37160371 DOI: 10.1136/bmjgh-2022-010435

While artificial intelligence (AI) offers promising solutions in healthcare, it also poses a number of threats to human health and well-being via social, political, economic and security-related determinants of health. We describe three such main ways misused narrow AI serves as a threat to human health: through increasing opportunities for control and manipulation of people; enhancing and dehumanising lethal weapon capacity and by rendering human labour increasingly obsolescent. We then examine self-improving 'artificial general intelligence' (AGI) and how this could pose an existential threat to humanity itself. Finally, we discuss the critical need for effective regulation, including the prohibition of certain types and applications of AI, and echo calls for a moratorium on the development of self-improving AGI. We ask the medical and public health community to engage in evidence-based advocacy for safe AI, rooted in the precautionary principle.

Matched MeSH terms: Artificial Intelligence*
Theoretically-derived molecular descriptors important in human intestinal absorption

Agatonovic-Kustrin S, Beresford R, Yusof AP

J Pharm Biomed Anal, 2001 May;25(2):227-37.
PMID: 11275432

A quantitative structure-human intestinal absorption relationship was developed using artificial neural network (ANN) modeling. A set of 86 drug compounds and their experimentally-derived intestinal absorption values used in this study was gathered from the literature and a total of 57 global molecular descriptors, including constitutional, topological, chemical, geometrical and quantum chemical descriptors, calculated for each compound. A supervised network with radial basis transfer function was used to correlate calculated molecular descriptors with experimentally-derived measures of human intestinal absorption. A genetic algorithm was then used to select important molecular descriptors. Intestinal absorption values (IA%) were used as the ANN's output and calculated molecular descriptors as the inputs. The best genetic neural network (GNN) model with 15 input descriptors was chosen, and the significance of the selected descriptors for intestinal absorption examined. Results obtained with the model that was developed indicate that lipophilicity, conformational stability and inter-molecular interactions (polarity, and hydrogen bonding) have the largest impact on intestinal absorption.

Matched MeSH terms: Artificial Intelligence
The role of artificial intelligence in the battle against antimicrobial-resistant bacteria

Lau HJ, Lim CH, Foo SC, Tan HS

Curr Genet, 2021 Jun;67(3):421-429.
PMID: 33585980 DOI: 10.1007/s00294-021-01156-5

Antimicrobial resistance (AMR) in bacteria is a global health crisis due to the rapid emergence of multidrug-resistant bacteria and the lengthy development of new antimicrobials. In light of this, artificial intelligence in the form of machine learning has been viewed as a potential counter to delay the spread of AMR. With the aid of AI, there are possibilities to predict and identify AMR in bacteria efficiently. Furthermore, a combination of machine learning algorithms and lab testing can help to accelerate the process of discovering new antimicrobials. To date, many machine learning algorithms for antimicrobial-resistance discovery had been created and vigorously validated. Most of these algorithms produced accurate results and outperformed the traditional methods which relied on sequence comparison within a database. This mini-review will provide an updated overview of antimicrobial design workflow using the latest machine-learning antimicrobial discovery algorithms in the last 5 years. With this review, we hope to improve upon the current AMR identification and antimicrobial development techniques by introducing the use of AI into the mix, including how the algorithms could be made more effective.

Matched MeSH terms: Artificial Intelligence*
The potential role of artificial intelligence-assisted chest X-ray imaging in detecting early-stage lung cancer in the community-a proposed algorithm for lung cancer screening in Malaysia

Sachithanandan A, Lockman H, Azman RR, Tho LM, Ban EZ, Ramon V

Med J Malaysia, 2024 Jan;79(1):9-14.
PMID: 38287751

INTRODUCTION: The poor prognosis of lung cancer has been largely attributed to the fact that most patients present with advanced stage disease. Although low dose computed tomography (LDCT) is presently considered the optimal imaging modality for lung cancer screening, its use has been hampered by cost and accessibility. One possible approach to facilitate lung cancer screening is to implement a risk-stratification step with chest radiography, given its ease of access and affordability. Furthermore, implementation of artificial-intelligence (AI) in chest radiography is expected to improve the detection of indeterminate pulmonary nodules, which may represent early lung cancer.
MATERIALS AND METHODS: This consensus statement was formulated by a panel of five experts of primary care and specialist doctors. A lung cancer screening algorithm was proposed for implementation locally.
RESULTS: In an earlier pilot project collaboration, AI-assisted chest radiography had been incorporated into lung cancer screening in the community. Preliminary experience in the pilot project suggests that the system is easy to use, affordable and scalable. Drawing from experience with the pilot project, a standardised lung cancer screening algorithm using AI in Malaysia was proposed. Requirements for such a screening programme, expected outcomes and limitations of AI-assisted chest radiography were also discussed.
CONCLUSION: The combined strategy of AI-assisted chest radiography and complementary LDCT imaging has great potential in detecting early-stage lung cancer in a timely manner, and irrespective of risk status. The proposed screening algorithm provides a guide for clinicians in Malaysia to participate in screening efforts.

Matched MeSH terms: Artificial Intelligence
Fulltext The potential of blockchain technology for occupational safety and health management system

Rafizah Musa, Mohamad Syazli Fathib

Journal of Occupational Safety and Health, 2019;16(1):27-31.
MyJurnal

Industries in Malaysia are entering a period of major disruption caused by new technologies such as Artificial Intelligent, Robotics, Blockchain, Nanotechnology as well as Building Information Modelling (BIM) and the Internet of Things (IoT). In this fourth industrial revolution where information is generated and exchanged at a rapid and huge scale, its reliability is of paramount importance. The success of Occupational Safety & Health Management System (OSHMS) is highly dependent on the reliability of the information gathered and used, where a large number of intermediaries authenticate the information to establish trust between the stakeholders. Blockchain technology is able to do verification by virtue of secured distributed storage brings about a paradigm shift in the way we establish trust. This paper gives an overview of the potential use of Blockchain technology for Occupational Safety & Health Management System. The discussions focused on the benefits and challenges of implementing the Blockchain technology in OSHMS. The conclusion is drawn based on the strength in the characteristics provided by the Blockchain technology itself.

Matched MeSH terms: Artificial Intelligence
The perceptions of medical physicists towards relevance and impact of artificial intelligence

Santos JC, Wong JHD, Pallath V, Ng KH

Phys Eng Sci Med, 2021 Sep;44(3):833-841.
PMID: 34283393 DOI: 10.1007/s13246-021-01036-9

Artificial intelligence (AI) is an innovative tool with the potential to impact medical physicists' clinical practices, research, and the profession. The relevance of AI and its impact on the clinical practice and routine of professionals in medical physics were evaluated by medical physicists and researchers in this field. An online survey questionnaire was designed for distribution to professionals and students in medical physics around the world. In addition to demographics questions, we surveyed opinions on the role of AI in medical physicists' practices, the possibility of AI threatening/disrupting the medical physicists' practices and career, the need for medical physicists to acquire knowledge on AI, and the need for teaching AI in postgraduate medical physics programmes. The level of knowledge of medical physicists on AI was also consulted. A total of 1019 respondents from 94 countries participated. More than 85% of the respondents agreed that AI would play an essential role in medical physicists' practices. AI should be taught in the postgraduate medical physics programmes, and that more applications such as quality control (QC), treatment planning would be performed by AI. Half of the respondents thought AI would not threaten/disrupt the medical physicists' practices. AI knowledge was mainly acquired through self-taught and work-related activities. Nonetheless, many (40%) reported that they have no skill in AI. The general perception of medical physicists was that AI is here to stay, influencing our practices. Medical physicists should be prepared with education and training for this new reality.

Matched MeSH terms: Artificial Intelligence*
Fulltext The contribution of particle swarm optimization to three-dimensional slope stability analysis

Kalatehjari R, Rashid AS, Ali N, Hajihassani M

ScientificWorldJournal, 2014;2014:973093.
PMID: 24991652 DOI: 10.1155/2014/973093

Over the last few years, particle swarm optimization (PSO) has been extensively applied in various geotechnical engineering including slope stability analysis. However, this contribution was limited to two-dimensional (2D) slope stability analysis. This paper applied PSO in three-dimensional (3D) slope stability problem to determine the critical slip surface (CSS) of soil slopes. A detailed description of adopted PSO was presented to provide a good basis for more contribution of this technique to the field of 3D slope stability problems. A general rotating ellipsoid shape was introduced as the specific particle for 3D slope stability analysis. A detailed sensitivity analysis was designed and performed to find the optimum values of parameters of PSO. Example problems were used to evaluate the applicability of PSO in determining the CSS of 3D slopes. The first example presented a comparison between the results of PSO and PLAXI-3D finite element software and the second example compared the ability of PSO to determine the CSS of 3D slopes with other optimization methods from the literature. The results demonstrated the efficiency and effectiveness of PSO in determining the CSS of 3D soil slopes.

Matched MeSH terms: Artificial Intelligence*
Fulltext The communication of artificial intelligence and deep learning in computer tomography image recognition of epidemic pulmonary infectious diseases

Wang W, Zhao X, Jia Y, Xu J

PLoS One, 2024;19(2):e0297578.
PMID: 38319912 DOI: 10.1371/journal.pone.0297578

The objectives are to improve the diagnostic efficiency and accuracy of epidemic pulmonary infectious diseases and to study the application of artificial intelligence (AI) in pulmonary infectious disease diagnosis and public health management. The computer tomography (CT) images of 200 patients with pulmonary infectious disease are collected and input into the AI-assisted diagnosis software based on the deep learning (DL) model, "UAI, pulmonary infectious disease intelligent auxiliary analysis system", for lesion detection. By analyzing the principles of convolutional neural networks (CNN) in deep learning (DL), the study selects the AlexNet model for the recognition and classification of pulmonary infection CT images. The software automatically detects the pneumonia lesions, marks them in batches, and calculates the lesion volume. The result shows that the CT manifestations of the patients are mainly involved in multiple lobes and density, the most common shadow is the ground-glass opacity. The detection rate of the manual method is 95.30%, the misdetection rate is 0.20% and missed diagnosis rate is 4.50%; the detection rate of the DL-based AI-assisted lesion method is 99.76%, the misdetection rate is 0.08%, and the missed diagnosis rate is 0.08%. Therefore, the proposed model can effectively identify pulmonary infectious disease lesions and provide relevant data information to objectively diagnose pulmonary infectious disease and manage public health.

Matched MeSH terms: Artificial Intelligence

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