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Fulltext Primary stability recognition of the newly designed cementless femoral stem using digital signal processing

Baharuddin MY, Salleh ShH, Hamedi M, Zulkifly AH, Lee MH, Mohd Noor A, et al.

Biomed Res Int, 2014;2014:478248.
PMID: 24800230 DOI: 10.1155/2014/478248

Stress shielding and micromotion are two major issues which determine the success of newly designed cementless femoral stems. The correlation of experimental validation with finite element analysis (FEA) is commonly used to evaluate the stress distribution and fixation stability of the stem within the femoral canal. This paper focused on the applications of feature extraction and pattern recognition using support vector machine (SVM) to determine the primary stability of the implant. We measured strain with triaxial rosette at the metaphyseal region and micromotion with linear variable direct transducer proximally and distally using composite femora. The root mean squares technique is used to feed the classifier which provides maximum likelihood estimation of amplitude, and radial basis function is used as the kernel parameter which mapped the datasets into separable hyperplanes. The results showed 100% pattern recognition accuracy using SVM for both strain and micromotion. This indicates that DSP could be applied in determining the femoral stem primary stability with high pattern recognition accuracy in biomechanical testing.

Matched MeSH terms: Artificial Intelligence
Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models

Bhagat SK, Tiyasha T, Awadh SM, Tung TM, Jawad AH, Yaseen ZM

Environ Pollut, 2021 Jan 01;268(Pt B):115663.
PMID: 33120144 DOI: 10.1016/j.envpol.2020.115663

Hybrid artificial intelligence (AI) models are developed for sediment lead (Pb) prediction in two Bays (i.e., Bramble (BB) and Deception (DB)) stations, Australia. A feature selection (FS) algorithm called extreme gradient boosting (XGBoost) is proposed to abstract the correlated input parameters for the Pb prediction and validated against principal component of analysis (PCA), recursive feature elimination (RFE), and the genetic algorithm (GA). XGBoost model is applied using a grid search strategy (Grid-XGBoost) for predicting Pb and validated against the commonly used AI models, artificial neural network (ANN) and support vector machine (SVM). The input parameter selection approaches redimensioned the 21 parameters into 9-5 parameters without losing their learned information over the models' training phase. At the BB station, the mean absolute percentage error (MAPE) values (0.06, 0.32, 0.34, and 0.33) were achieved for the XGBoost-SVM, XGBoost-ANN, XGBoost-Grid-XGBoost, and Grid-XGBoost models, respectively. At the DB station, the lowest MAPE values, 0.25 and 0.24, were attained for the XGBoost-Grid-XGBoost and Grid-XGBoost models, respectively. Overall, the proposed hybrid AI models provided a reliable and robust computer aid technology for sediment Pb prediction that contribute to the best knowledge of environmental pollution monitoring and assessment.

Matched MeSH terms: Artificial Intelligence*
Prediction of copper ions adsorption by attapulgite adsorbent using tuned-artificial intelligence model

Bhagat SK, Pyrgaki K, Salih SQ, Tiyasha T, Beyaztas U, Shahid S, et al.

Chemosphere, 2021 Aug;276:130162.
PMID: 34088083 DOI: 10.1016/j.chemosphere.2021.130162

Copper (Cu) ion in wastewater is considered as one of the crucial hazardous elements to be quantified. This research is established to predict copper ions adsorption (Ad) by Attapulgite clay from aqueous solutions using computer-aided models. Three artificial intelligent (AI) models are developed for this purpose including Grid optimization-based random forest (Grid-RF), artificial neural network (ANN) and support vector machine (SVM). Principal component analysis (PCA) is used to select model inputs from different variables including the initial concentration of Cu (IC), the dosage of Attapulgite clay (Dose), contact time (CT), pH, and addition of NaNO3 (SN). The ANN model is found to predict Ad with minimum root mean square error (RMSE = 0.9283) and maximum coefficient of determination (R2 = 0.9974) when all the variables (i.e., IC, Dose, CT, pH, SN) were considered as input. The prediction accuracy of Grid-RF model is found similar to ANN model when a few numbers of predictors are used. According to prediction accuracy, the models can be arranged as ANN-M5> Grid-RF-M5> Grid-RF-M4> ANN-M4> SVM-M4> SVM-M5. Overall, the applied statistical analysis of the results indicates that ANN and Grid-RF models can be employed as a computer-aided model for monitoring and simulating the adsorption from aqueous solutions by Attapulgite clay.

Matched MeSH terms: Artificial Intelligence*
Fulltext Artificial intelligence enabled healthcare: A hype, hope or harm

Bhattacharya S, Pradhan KB, Bashar MA, Tripathi S, Semwal J, Marzo RR, et al.

J Family Med Prim Care, 2019 Nov;8(11):3461-3464.
PMID: 31803636 DOI: 10.4103/jfmpc.jfmpc_155_19

In this paper, we have described the health care problem (maldistribution of doctors) in India. Later, we have introduced the concept of artificial intelligence and we have described this technology with various examples, how it is rapidly changing the health care scenario across the world. We have also described the various advantages of artificial intelligence technology. At the end of the paper, we have raised some serious concerns regarding complete replacement of human based health care technology with artificial intelligence technology. Lastly, we concluded that we have to use artificial intelligent technology to prevent human sufferings/health care problems with proper caution.

Matched MeSH terms: Artificial Intelligence
Development of a control system for artificially rehabilitated limbs: a review

Bhuiyan MS, Choudhury IA, Dahari M

Biol Cybern, 2015 Apr;109(2):141-62.
PMID: 25491411 DOI: 10.1007/s00422-014-0635-1

Development of an advanced control system for prostheses (artificial limbs) is necessary to provide functionality, effectiveness, and preferably the feeling of a sound living limb. The development of the control system has introduced varieties of control strategies depending on the application. This paper reviews some control systems used for prosthetics, orthotics, and exoskeletons. The advantages and limitations of different control systems for particular applications have been discussed and presented in a comparative manner to help in deciding the appropriate method for pertinent application.

Matched MeSH terms: Artificial Intelligence
Edge AI-Based Automated Detection and Classification of Road Anomalies in VANET Using Deep Learning

Bibi R, Saeed Y, Zeb A, Ghazal TM, Rahman T, Said RA, et al.

Comput Intell Neurosci, 2021;2021:6262194.
PMID: 34630550 DOI: 10.1155/2021/6262194

Road surface defects are crucial problems for safe and smooth traffic flow. Due to climate changes, low quality of construction material, large flow of traffic, and heavy vehicles, road surface anomalies are increasing rapidly. Detection and repairing of these defects are necessary for the safety of drivers, passengers, and vehicles from mechanical faults. In this modern era, autonomous vehicles are an active research area that controls itself with the help of in-vehicle sensors without human commands, especially after the emergence of deep learning (DNN) techniques. A combination of sensors and DNN techniques can be useful for unmanned vehicles for the perception of their surroundings for the detection of tracks and obstacles for smooth traveling based on the deployment of artificial intelligence in vehicles. One of the biggest challenges for autonomous vehicles is to avoid the critical road defects that may lead to dangerous situations. To solve the accident issues and share emergency information, the Intelligent Transportation System (ITS) introduced the concept of vehicular network termed as vehicular ad hoc network (VANET) for achieving security and safety in a traffic flow. A novel mechanism is proposed for the automatic detection of road anomalies by autonomous vehicles and providing road information to upcoming vehicles based on Edge AI and VANET. Road images captured via camera and deployment of the trained model for road anomaly detection in a vehicle could help to reduce the accident rate and risk of hazards on poor road conditions. The techniques Residual Convolutional Neural Network (ResNet-18) and Visual Geometry Group (VGG-11) are applied for the automatic detection and classification of the road with anomalies such as a pothole, bump, crack, and plain roads without anomalies using the dataset from different online sources. The results show that the applied models performed well than other techniques used for road anomalies identification.

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*
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*
Rosline Hassan: building the future of haemato-pathology in Malaysia

Burki TK

Lancet Haematol, 2021 Aug;8(8):e551.
PMID: 34329575 DOI: 10.1016/S2352-3026(21)00215-5
Matched MeSH terms: Artificial Intelligence
Genomic instantiation of consciousness in neurons through a biophoton field theory

Cacha LA, Poznanski RR

J Integr Neurosci, 2014 Jun;13(2):253-92.
PMID: 25012712 DOI: 10.1142/S0219635214400081

A theoretical framework is developed based on the premise that brains evolved into sufficiently complex adaptive systems capable of instantiating genomic consciousness through self-awareness and complex interactions that recognize qualitatively the controlling factors of biological processes. Furthermore, our hypothesis assumes that the collective interactions in neurons yield macroergic effects, which can produce sufficiently strong electric energy fields for electronic excitations to take place on the surface of endogenous structures via alpha-helical integral proteins as electro-solitons. Specifically the process of radiative relaxation of the electro-solitons allows for the transfer of energy via interactions with deoxyribonucleic acid (DNA) molecules to induce conformational changes in DNA molecules producing an ultra weak non-thermal spontaneous emission of coherent biophotons through a quantum effect. The instantiation of coherent biophotons confined in spaces of DNA molecules guides the biophoton field to be instantaneously conducted along the axonal and neuronal arbors and in-between neurons and throughout the cerebral cortex (cortico-thalamic system) and subcortical areas (e.g., midbrain and hindbrain). Thus providing an informational character of the electric coherence of the brain - referred to as quantum coherence. The biophoton field is realized as a conscious field upon the re-absorption of biophotons by exciplex states of DNA molecules. Such quantum phenomenon brings about self-awareness and enables objectivity to have access to subjectivity in the unconscious. As such, subjective experiences can be recalled to consciousness as subjective conscious experiences or qualia through co-operative interactions between exciplex states of DNA molecules and biophotons leading to metabolic activity and energy transfer across proteins as a result of protein-ligand binding during protein-protein communication. The biophoton field as a conscious field is attributable to the resultant effect of specifying qualia from the metabolic energy field that is transported in macromolecular proteins throughout specific networks of neurons that are constantly transforming into more stable associable representations as molecular solitons. The metastability of subjective experiences based on resonant dynamics occurs when bottom-up patterns of neocortical excitatory activity are matched with top-down expectations as adaptive dynamic pressures. These dynamics of on-going activity patterns influenced by the environment and selected as the preferred subjective experience in terms of a functional field through functional interactions and biological laws are realized as subjectivity and actualized through functional integration as qualia. It is concluded that interactionism and not information processing is the key in understanding how consciousness bridges the explanatory gap between subjective experiences and their neural correlates in the transcendental brain.

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*
Fulltext Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods

Chang SW, Abdul-Kareem S, Merican AF, Zain RB

BMC Bioinformatics, 2013;14:170.
PMID: 23725313 DOI: 10.1186/1471-2105-14-170

Machine learning techniques are becoming useful as an alternative approach to conventional medical diagnosis or prognosis as they are good for handling noisy and incomplete data, and significant results can be attained despite a small sample size. Traditionally, clinicians make prognostic decisions based on clinicopathologic markers. However, it is not easy for the most skilful clinician to come out with an accurate prognosis by using these markers alone. Thus, there is a need to use genomic markers to improve the accuracy of prognosis. The main aim of this research is to apply a hybrid of feature selection and machine learning methods in oral cancer prognosis based on the parameters of the correlation of clinicopathologic and genomic markers.

Matched MeSH terms: Artificial Intelligence*
A hybrid prognostic model for oral cancer based on clinicopathologic and genomic markers

Chang SW, Merican AFMA, Rosnah Zain, Kareem SA

Sains Malaysiana, 2014;43:567-573.

There are very few prognostic studies that combine both clinicopathologic and genomic data. Most of the studies use only clinicopathologic factors without taking into consideration the tumour biology and molecular information, while some studies use genomic markers or microarray information only without the clinicopathologic parameters. Thus, these studies may not be able to prognoses a patient effectively. Previous studies have shown that prognosis results are more accurate when using both clinicopathologic and genomic data. The objectives of this research were to apply hybrid artificial intelligent techniques in the prognosis of oral cancer based on the correlation of clinicopathologic and genomic markers and to prove that the prognosis is better with both markers. The proposed hybrid model consisting of two stages, where stage one with ReliefF-GA feature selection method to find an optimal feature of subset and stage two with ANFIS classification to classify either the patients alive or dead after certain years of diagnosis. The proposed prognostic model was experimented on two groups of oral cancer dataset collected locally here in Malaysia, Group 1 with clinicopathologic markers only and Group 2 with both clinicopathologic and genomic markers. The results proved that the proposed model with optimum features selected is more accurate with the use of both clinicopathologic and genomic markers and outperformed the other methods of artificial neural network, support vector machine and logistic regression. This prognostic model is feasible to aid the clinicians in the decision support stage and to identify the high risk markers to better predict the survival rate for each oral cancer patient.

Matched MeSH terms: Artificial Intelligence
Fulltext Accuracy of Artificial Intelligence-Based Photographic Detection of Gingivitis

Chau RCW, Li GH, Tew IM, Thu KM, McGrath C, Lo WL, et al.

Int Dent J, 2023 Oct;73(5):724-730.
PMID: 37117096 DOI: 10.1016/j.identj.2023.03.007

OBJECTIVES: Gingivitis is one of the most prevalent plaque-initiated dental diseases globally. It is challenging to maintain satisfactory plaque control without continuous professional advice. Artificial intelligence may be used to provide automated visual plaque control advice based on intraoral photographs.
METHODS: Frontal view intraoral photographs fulfilling selection criteria were collected. Along the gingival margin, the gingival conditions of individual sites were labelled as healthy, diseased, or questionable. Photographs were randomly assigned as training or validation datasets. Training datasets were input into a novel artificial intelligence system and its accuracy in detection of gingivitis including sensitivity, specificity, and mean intersection-over-union were analysed using validation dataset. The accuracy was reported according to STARD-2015 statement.
RESULTS: A total of 567 intraoral photographs were collected and labelled, of which 80% were used for training and 20% for validation. Regarding training datasets, there were total 113,745,208 pixels with 9,270,413; 5,711,027; and 4,596,612 pixels were labelled as healthy, diseased, and questionable respectively. Regarding validation datasets, there were 28,319,607 pixels with 1,732,031; 1,866,104; and 1,116,493 pixels were labelled as healthy, diseased, and questionable, respectively. AI correctly predicted 1,114,623 healthy and 1,183,718 diseased pixels with sensitivity of 0.92 and specificity of 0.94. The mean intersection-over-union of the system was 0.60 and above the commonly accepted threshold of 0.50.
CONCLUSIONS: Artificial intelligence could identify specific sites with and without gingival inflammation, with high sensitivity and high specificity that are on par with visual examination by human dentist. This system may be used for monitoring of the effectiveness of patients' plaque control.

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 Testing the Feasibility and Acceptability of Using an Artificial Intelligence Chatbot to Promote HIV Testing and Pre-Exposure Prophylaxis in Malaysia: Mixed Methods Study

Cheah MH, Gan YN, Altice FL, Wickersham JA, Shrestha R, Salleh NAM, et al.

JMIR Hum Factors, 2024 Jan 26;11:e52055.
PMID: 38277206 DOI: 10.2196/52055

BACKGROUND: The HIV epidemic continues to grow fastest among men who have sex with men (MSM) in Malaysia in the presence of stigma and discrimination. Engaging MSM on the internet using chatbots supported through artificial intelligence (AI) can potentially help HIV prevention efforts. We previously identified the benefits, limitations, and preferred features of HIV prevention AI chatbots and developed an AI chatbot prototype that is now tested for feasibility and acceptability.
OBJECTIVE: This study aims to test the feasibility and acceptability of an AI chatbot in promoting the uptake of HIV testing and pre-exposure prophylaxis (PrEP) in MSM.
METHODS: We conducted beta testing with 14 MSM from February to April 2022 using Zoom (Zoom Video Communications, Inc). Beta testing involved 3 steps: a 45-minute human-chatbot interaction using the think-aloud method, a 35-minute semistructured interview, and a 10-minute web-based survey. The first 2 steps were recorded, transcribed verbatim, and analyzed using the Unified Theory of Acceptance and Use of Technology. Emerging themes from the qualitative data were mapped on the 4 domains of the Unified Theory of Acceptance and Use of Technology: performance expectancy, effort expectancy, facilitating conditions, and social influence.
RESULTS: Most participants (13/14, 93%) perceived the chatbot to be useful because it provided comprehensive information on HIV testing and PrEP (performance expectancy). All participants indicated that the chatbot was easy to use because of its simple, straightforward design and quick, friendly responses (effort expectancy). Moreover, 93% (13/14) of the participants rated the overall chatbot quality as high, and all participants perceived the chatbot as a helpful tool and would refer it to others. Approximately 79% (11/14) of the participants agreed they would continue using the chatbot. They suggested adding a local language (ie, Bahasa Malaysia) to customize the chatbot to the Malaysian context (facilitating condition) and suggested that the chatbot should also incorporate more information on mental health, HIV risk assessment, and consequences of HIV. In terms of social influence, all participants perceived the chatbot as helpful in avoiding stigma-inducing interactions and thus could increase the frequency of HIV testing and PrEP uptake among MSM.
CONCLUSIONS: The current AI chatbot is feasible and acceptable to promote the uptake of HIV testing and PrEP. To ensure the successful implementation and dissemination of AI chatbots in Malaysia, they should be customized to communicate in Bahasa Malaysia and upgraded to provide other HIV-related information to improve usability, such as mental health support, risk assessment for sexually transmitted infections, AIDS treatment, and the consequences of contracting HIV.

Matched MeSH terms: Artificial Intelligence
Healthcare knowledge acquisition: an ontology-based approach using the extensible markup language (XML)

Cheah YN, Abidi SS

Stud Health Technol Inform, 2000;77:827-31.
PMID: 11187669

The healthcare enterprise requires a great deal of knowledge to maintain premium efficiency in the delivery of quality healthcare. We employ Knowledge Management based knowledge acquisition strategies to procure 'tacit' healthcare knowledge from experienced healthcare practitioners. Situational, problem-specific Scenarios are proposed as viable knowledge acquisition and representation constructs. We present a healthcare Tacit Knowledge Acquisition Info-structure (TKAI) that allows remote healthcare practitioners to record their tacit knowledge. TKAI employs (a) ontologies for standardisation of tacit knowledge and (b) XML to represent scenario instances for their transfer over the Internet to the server-side Scenario-Base and for the global sharing of acquired tacit healthcare knowledge.

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 Healthcare knowledge management through building and operationalising healthcare enterprise memory

Cheah YN, Abidi SS

Stud Health Technol Inform, 1999;68:726-30.
PMID: 10724990

In this paper we suggest that the healthcare enterprise needs to be more conscious of its vast knowledge resources vis-à-vis the exploitation of knowledge management techniques to efficiently manage its knowledge. The development of healthcare enterprise memory is suggested as a solution, together with a novel approach advocating the operationalisation of healthcare enterprise memories leading to the modelling of healthcare processes for strategic planning. As an example, we present a simulation of Service Delivery Time in a hospital's OPD.

Matched MeSH terms: Artificial Intelligence*
Fulltext The Potential of Chatbots for Emotional Support and Promoting Mental Well-Being in Different Cultures: Mixed Methods Study

Chin H, Song H, Baek G, Shin M, Jung C, Cha M, et al.

J Med Internet Res, 2023 Oct 20;25:e51712.
PMID: 37862063 DOI: 10.2196/51712

BACKGROUND: Artificial intelligence chatbot research has focused on technical advances in natural language processing and validating the effectiveness of human-machine conversations in specific settings. However, real-world chat data remain proprietary and unexplored despite their growing popularity, and new analyses of chatbot uses and their effects on mitigating negative moods are urgently needed.
OBJECTIVE: In this study, we investigated whether and how artificial intelligence chatbots facilitate the expression of user emotions, specifically sadness and depression. We also examined cultural differences in the expression of depressive moods among users in Western and Eastern countries.
METHODS: This study used SimSimi, a global open-domain social chatbot, to analyze 152,783 conversation utterances containing the terms "depress" and "sad" in 3 Western countries (Canada, the United Kingdom, and the United States) and 5 Eastern countries (Indonesia, India, Malaysia, the Philippines, and Thailand). Study 1 reports new findings on the cultural differences in how people talk about depression and sadness to chatbots based on Linguistic Inquiry and Word Count and n-gram analyses. In study 2, we classified chat conversations into predefined topics using semisupervised classification techniques to better understand the types of depressive moods prevalent in chats. We then identified the distinguishing features of chat-based depressive discourse data and the disparity between Eastern and Western users.
RESULTS: Our data revealed intriguing cultural differences. Chatbot users in Eastern countries indicated stronger emotions about depression than users in Western countries (positive: P

Matched MeSH terms: Artificial Intelligence*

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