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No person is an island: Unpacking the work and after-work consequences of interacting with artificial intelligence

Tang PM, Koopman J, Mai KM, De Cremer D, Zhang JH, Reynders P, et al.

J Appl Psychol, 2023 Nov;108(11):1766-1789.
PMID: 37307359 DOI: 10.1037/apl0001103

The artificial intelligence (AI) revolution has arrived, as AI systems are increasingly being integrated across organizational functions into the work lives of employees. This coupling of employees and machines fundamentally alters the work-related interactions to which employees are accustomed, as employees find themselves increasingly interacting with, and relying on, AI systems instead of human coworkers. This increased coupling of employees and AI portends a shift toward more of an "asocial system," wherein people may feel socially disconnected at work. Drawing upon the social affiliation model, we develop a model delineating both adaptive and maladaptive consequences of this situation. Specifically, we theorize that the more employees interact with AI in the pursuit of work goals, the more they experience a need for social affiliation (adaptive)-which may contribute to more helping behavior toward coworkers at work-as well as a feeling of loneliness (maladaptive), which then further impair employee well-being after work (i.e., more insomnia and alcohol consumption). In addition, we submit that these effects should be especially pronounced among employees with higher levels of attachment anxiety. Results across four studies (N = 794) with mixed methodologies (i.e., survey study, field experiment, and simulation study; Studies 1-4) with employees from four different regions (i.e., Taiwan, Indonesia, United States, and Malaysia) generally support our hypotheses. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Matched MeSH terms: Artificial Intelligence*
Fulltext Effectiveness of Artificial Intelligence (AI) in Improving Pupils' Deep Learning in Primary School Mathematics Teaching in Fujian Province

Qiu Y, Pan J, Ishak NA

Comput Intell Neurosci, 2022;2022:1362996.
PMID: 36193186 DOI: 10.1155/2022/1362996

Several primary school students in Fujian Province have perceived studying mathematics as challenging. To deal with this issue, computer technology advancements, specifically artificial intelligence (AI), present an opportunity to evaluate individual students' learning challenges and give individualized support to optimize their success in mathematics classes. It is also possible to use virtual reality (VR) to assist learners in acquiring complex mathematical and logical ideas and to lessen learners' mistakes. As a result, researchers, particularly beginners, are missing out on a complete perspective of the study of AI in teaching mathematics. That is why we are exploring the role of AI in math education by developing a "fuzzy-based tweakable convolution neural network with a long short-term memory (FT-CNN-LSTM-AM)" method. For this investigation, the students' datasets are taken and educated by mathematical teaching via the application of AI. The proposed method is utilized to predict the students' performance in mathematical education. A grey wolf optimizer is employed to boost the effectiveness of the proposed method. Furthermore, the performance of the proposed method is analyzed and compared with existing approaches to gain the highest reliability.

Matched MeSH terms: Artificial Intelligence*
Fulltext Blockchain and Machine Learning Inspired Secure Smart Home Communication Network

Menon S, Anand D, Kavita, Verma S, Kaur M, Jhanjhi NZ, et al.

Sensors (Basel), 2023 Jul 04;23(13).
PMID: 37447981 DOI: 10.3390/s23136132

With the increasing growth rate of smart home devices and their interconnectivity via the Internet of Things (IoT), security threats to the communication network have become a concern. This paper proposes a learning engine for a smart home communication network that utilizes blockchain-based secure communication and a cloud-based data evaluation layer to segregate and rank data on the basis of three broad categories of Transactions (T), namely Smart T, Mod T, and Avoid T. The learning engine utilizes a neural network for the training and classification of the categories that helps the blockchain layer with improvisation in the decision-making process. The contributions of this paper include the application of a secure blockchain layer for user authentication and the generation of a ledger for the communication network; the utilization of the cloud-based data evaluation layer; the enhancement of an SI-based algorithm for training; and the utilization of a neural engine for the precise training and classification of categories. The proposed algorithm outperformed the Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system, the data fusion technique, and artificial intelligence Internet of Things technology in providing electronic information engineering and analyzing optimization schemes in terms of the computation complexity, false authentication rate, and qualitative parameters with a lower average computation complexity; in addition, it ensures a secure, efficient smart home communication network to enhance the lifestyle of human beings.

Matched MeSH terms: Artificial Intelligence*
Techno-economic optimization of a new waste-to-energy plant for electricity, cooling, and desalinated water using various biomass for emission reduction

Hai T, Ma X, Singh Chauhan B, Mahmoud S, Al-Kouz W, Tong J, et al.

Chemosphere, 2023 Oct;338:139398.
PMID: 37406939 DOI: 10.1016/j.chemosphere.2023.139398

A newly developed waste-to-energy system using a biomass combined energy system designed and taken into account for electricity generation, cooling, and freshwater production has been investigated and modeled in this project. The investigated system incorporates several different cycles, such as a biomass waste integrated gasifier-gas turbine cycle, a high-temperature fuel cell, a Rankine cycle, an absorption refrigeration system, and a flash distillation system for seawater desalination. The EES software is employed to perform a basic analysis of the system. They are then transferred to MATLAB software to optimize and evaluate the impact of operational factors. Artificial intelligence is employed to evaluate and model the EES software's analysis output for this purpose. By enhancing the flow rate of fuel from 4 to 6.5 kg/s, the cost rate and energy efficiency are reduced by 51% and increased by 6.5%, respectively. Furthermore, the maximum increment in exergetic efficiency takes place whenever the inlet temperature of the gas turbine rises. According to an analysis of three types of biomasses, Solid Waste possesses the maximum efficiency rate, work output, and expense. Rice Husk, in contrast, has the minimum efficiency, work output, and expense. Additionally, with the change in fuel discharge and gas turbine inlet temperature, the system behavior for all three types of biomasses will be nearly identical. The Pareto front optimization findings demonstrate that the best mode for system performance is an output power of 53,512 kW, a cost of 0.643 dollars per second, and a first law efficiency of 42%. This optimal value occurs for fuel discharge of 5.125 and the maximum inlet temperature for a gas turbine. The rates of water desalination and cooling in this condition are 18.818 kg/s and 2356 kW, respectively.

Matched MeSH terms: Artificial Intelligence*
A Tête-à-tête with ChatGPT on the impact of artificial intelligence in medical education

Vignesh R, Pradeep P, Balakrishnan P

Med J Malaysia, 2023 Jul;78(4):547-549.
PMID: 37518931

Chat Generative Pre-Trained Transformer (ChatGPT) is an artificial intelligence (AI) language model developed by OpenAI. It is trained to process vast amounts of text and engage in human-like conversational interaction with users. Being accessible by all, it is widely used and its capabilities range from language translation, summarising long texts and creative writing. This article explores the potential role of ChatGPT in medical education and the possible concerns about the misuse of this technology through a conversation with ChatGPT itself via text prompts. The implications of this technology in medical education as told by ChatGPT are interesting and seemingly helpful for both the students and the tutors. However, this could be a double-edged sword considering the risks of compromised students' integrity and concerns of over-reliance. This also calls for counter strategies and policies in place to mitigate these risks.

Matched MeSH terms: Artificial Intelligence*
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 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*
The Potential of ChatGPT in Assisting Children with Down Syndrome

Yang J, Por LY, Leong MC, Ku CS

Ann Biomed Eng, 2023 Dec;51(12):2638-2640.
PMID: 37332002 DOI: 10.1007/s10439-023-03281-3

ChatGPT, an advanced language generation model developed by OpenAI, has the potential to revolutionize healthcare delivery and support for individuals with various conditions, including Down syndrome. This article explores the applications of ChatGPT in assisting children with Down syndrome, highlighting the benefits it can bring to their education, social interaction, and overall well-being. While acknowledging the challenges and limitations, we examine how ChatGPT can be utilized as a valuable tool in enhancing the lives of these children, promoting their cognitive development, and supporting their unique needs.

Matched MeSH terms: Artificial Intelligence*
New insights in improving sustainability in meat production: opportunities and challenges

Kumar P, Abubakar AA, Verma AK, Umaraw P, Adewale Ahmed M, Mehta N, et al.

Crit Rev Food Sci Nutr, 2023 Nov;63(33):11830-11858.
PMID: 35821661 DOI: 10.1080/10408398.2022.2096562

Treating livestock as senseless production machines has led to rampant depletion of natural resources, enhanced greenhouse gas emissions, gross animal welfare violations, and other ethical issues. It has essentially instigated constant scrutiny of conventional meat production by various experts and scientists. Sustainably in the meat sector is a big challenge which requires a multifaced and holistic approach. Novel tools like digitalization of the farming system and livestock market, precision livestock farming, application of remote sensing and artificial intelligence to manage production and environmental impact/GHG emission, can help in attaining sustainability in this sector. Further, improving nutrient use efficiency and recycling in feed and animal production through integration with agroecology and industrial ecology, improving individual animal and herd health by ensuring proper biosecurity measures and selective breeding, and welfare by mitigating animal stress during production are also key elements in achieving sustainability in meat production. In addition, sustainability bears a direct relationship with various social dimensions of meat production efficiency such as non-market attributes, balance between demand and consumption, market and policy failures. The present review critically examines the various aspects that significantly impact the efficiency and sustainability of meat production.

Matched MeSH terms: Artificial Intelligence*
Fulltext A multinational study on the factors influencing university students' attitudes and usage of ChatGPT

Abdaljaleel M, Barakat M, Alsanafi M, Salim NA, Abazid H, Malaeb D, et al.

Sci Rep, 2024 Jan 23;14(1):1983.
PMID: 38263214 DOI: 10.1038/s41598-024-52549-8

Artificial intelligence models, like ChatGPT, have the potential to revolutionize higher education when implemented properly. This study aimed to investigate the factors influencing university students' attitudes and usage of ChatGPT in Arab countries. The survey instrument "TAME-ChatGPT" was administered to 2240 participants from Iraq, Kuwait, Egypt, Lebanon, and Jordan. Of those, 46.8% heard of ChatGPT, and 52.6% used it before the study. The results indicated that a positive attitude and usage of ChatGPT were determined by factors like ease of use, positive attitude towards technology, social influence, perceived usefulness, behavioral/cognitive influences, low perceived risks, and low anxiety. Confirmatory factor analysis indicated the adequacy of the "TAME-ChatGPT" constructs. Multivariate analysis demonstrated that the attitude towards ChatGPT usage was significantly influenced by country of residence, age, university type, and recent academic performance. This study validated "TAME-ChatGPT" as a useful tool for assessing ChatGPT adoption among university students. The successful integration of ChatGPT in higher education relies on the perceived ease of use, perceived usefulness, positive attitude towards technology, social influence, behavioral/cognitive elements, low anxiety, and minimal perceived risks. Policies for ChatGPT adoption in higher education should be tailored to individual contexts, considering the variations in student attitudes observed in this study.

Matched MeSH terms: Artificial Intelligence*
Fulltext Artificial intelligence and the orthopaedic surgeon: A review of the literature and potential applications for future practice: Current concepts

Khoriati AA, Shahid Z, Fok M, Frank RM, Voss A, D'Hooghe P, et al.

J ISAKOS, 2024 Apr;9(2):227-233.
PMID: 37949113 DOI: 10.1016/j.jisako.2023.10.015
Matched MeSH terms: Artificial Intelligence*
Fulltext Position Statements of the Emerging Trends Committee of the Asian Oceanian Society of Radiology on the Adoption and Implementation of Artificial Intelligence for Radiology

Wee NK, Git KA, Lee WJ, Raval G, Pattokhov A, Ho ELM, et al.

Korean J Radiol, 2024 Jul;25(7):603-612.
PMID: 38942454 DOI: 10.3348/kjr.2024.0419

Artificial intelligence (AI) is rapidly gaining recognition in the radiology domain as a greater number of radiologists are becoming AI-literate. However, the adoption and implementation of AI solutions in clinical settings have been slow, with points of contention. A group of AI users comprising mainly clinical radiologists across various Asian countries, including India, Japan, Malaysia, Singapore, Taiwan, Thailand, and Uzbekistan, formed the working group. This study aimed to draft position statements regarding the application and clinical deployment of AI in radiology. The primary aim is to raise awareness among the general public, promote professional interest and discussion, clarify ethical considerations when implementing AI technology, and engage the radiology profession in the ever-changing clinical practice. These position statements highlight pertinent issues that need to be addressed between care providers and care recipients. More importantly, this will help legalize the use of non-human instruments in clinical deployment without compromising ethical considerations, decision-making precision, and clinical professional standards. We base our study on four main principles of medical care-respect for patient autonomy, beneficence, non-maleficence, and justice.

Matched MeSH terms: Artificial Intelligence*
A Novel Evaluation Framework for Medical LLMs: Combining Fuzzy Logic and MCDM for Medical Relation and Clinical Concept Extraction

Alamoodi AH, Zughoul O, David D, Garfan S, Pamucar D, Albahri OS, et al.

J Med Syst, 2024 Aug 31;48(1):81.
PMID: 39214943 DOI: 10.1007/s10916-024-02090-y

Artificial intelligence (AI) has become a crucial element of modern technology, especially in the healthcare sector, which is apparent given the continuous development of large language models (LLMs), which are utilized in various domains, including medical beings. However, when it comes to using these LLMs for the medical domain, there's a need for an evaluation platform to determine their suitability and drive future development efforts. Towards that end, this study aims to address this concern by developing a comprehensive Multi-Criteria Decision Making (MCDM) approach that is specifically designed to evaluate medical LLMs. The success of AI, particularly LLMs, in the healthcare domain, depends on their efficacy, safety, and ethical compliance. Therefore, it is essential to have a robust evaluation framework for their integration into medical contexts. This study proposes using the Fuzzy-Weighted Zero-InConsistency (FWZIC) method extended to p, q-quasirung orthopair fuzzy set (p, q-QROFS) for weighing evaluation criteria. This extension enables the handling of uncertainties inherent in medical decision-making processes. The approach accommodates the imprecise and multifaceted nature of real-world medical data and criteria by incorporating fuzzy logic principles. The MultiAtributive Ideal-Real Comparative Analysis (MAIRCA) method is employed for the assessment of medical LLMs utilized in the case study of this research. The results of this research revealed that "Medical Relation Extraction" criteria with its sub-levels had more importance with (0.504) than "Clinical Concept Extraction" with (0.495). For the LLMs evaluated, out of 6 alternatives, ( A 4 ) "GatorTron S 10B" had the 1st rank as compared to ( A 1 ) "GatorTron 90B" had the 6th rank. The implications of this study extend beyond academic discourse, directly impacting healthcare practices and patient outcomes. The proposed framework can help healthcare professionals make more informed decisions regarding the adoption and utilization of LLMs in medical settings.

Matched MeSH terms: Artificial Intelligence*
Fulltext Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique

Ali AM, Ghaleb FA, Al-Rimy BAS, Alsolami FJ, Khan AI

Sensors (Basel), 2022 Sep 15;22(18).
PMID: 36146319 DOI: 10.3390/s22186970

Recently, fake news has been widely spread through the Internet due to the increased use of social media for communication. Fake news has become a significant concern due to its harmful impact on individual attitudes and the community's behavior. Researchers and social media service providers have commonly utilized artificial intelligence techniques in the recent few years to rein in fake news propagation. However, fake news detection is challenging due to the use of political language and the high linguistic similarities between real and fake news. In addition, most news sentences are short, therefore finding valuable representative features that machine learning classifiers can use to distinguish between fake and authentic news is difficult because both false and legitimate news have comparable language traits. Existing fake news solutions suffer from low detection performance due to improper representation and model design. This study aims at improving the detection accuracy by proposing a deep ensemble fake news detection model using the sequential deep learning technique. The proposed model was constructed in three phases. In the first phase, features were extracted from news contents, preprocessed using natural language processing techniques, enriched using n-gram, and represented using the term frequency-inverse term frequency technique. In the second phase, an ensemble model based on deep learning was constructed as follows. Multiple binary classifiers were trained using sequential deep learning networks to extract the representative hidden features that could accurately classify news types. In the third phase, a multi-class classifier was constructed based on multilayer perceptron (MLP) and trained using the features extracted from the aggregated outputs of the deep learning-based binary classifiers for final classification. The two popular and well-known datasets (LIAR and ISOT) were used with different classifiers to benchmark the proposed model. Compared with the state-of-the-art models, which use deep contextualized representation with convolutional neural network (CNN), the proposed model shows significant improvements (2.41%) in the overall performance in terms of the F1score for the LIAR dataset, which is more challenging than other datasets. Meanwhile, the proposed model achieves 100% accuracy with ISOT. The study demonstrates that traditional features extracted from news content with proper model design outperform the existing models that were constructed based on text embedding techniques.

Matched MeSH terms: Artificial Intelligence*
Fulltext Systematic Literature Review on Visual Analytics of Predictive Maintenance in the Manufacturing Industry

Cheng X, Chaw JK, Goh KM, Ting TT, Sahrani S, Ahmad MN, et al.

Sensors (Basel), 2022 Aug 23;22(17).
PMID: 36080780 DOI: 10.3390/s22176321

The widespread adoption of cyber-physical systems and other cutting-edge digital technology in manufacturing industry production facilities may motivate stakeholders to embrace the idea of Industry 4.0. Some industrial companies already have different sensors installed on their machines; however, without proper analysis, the data collected is not useful. This systematic review's main goal is to synthesize the existing evidence on the application of predictive maintenance (PdM) with visual aids and to identify the key knowledge gaps in areas including utilities, power generation, industry, and energy consumption. After a thorough search and evaluation for relevancy, 37 documents were identified. Moreover, we identified the visual analytics of PdM, including anomaly detection, planning/scheduling, exploratory data analysis (EDA), and explainable artificial intelligence (XAI). The findings revealed that anomaly detection was a major domain in PdM-related works. We conclude that most of the literature lacks depth in terms of an overall framework that combines data-driven and knowledge-driven techniques of PdM in the manufacturing industry. Some works that utilized both techniques indicated promising results, but there is insufficient research on involving maintenance personnel's feedback in the latter stage of PdM architecture. Thus, there are still pertinent issues that need to be investigated, and limitations that need to be overcome before PdM is deployed with minimal human involvement.

Matched MeSH terms: Artificial Intelligence*
A review of risk prediction models in cardiovascular disease: conventional approach vs. artificial intelligent approach

Mohd Faizal AS, Thevarajah TM, Khor SM, Chang SW

Comput Methods Programs Biomed, 2021 Aug;207:106190.
PMID: 34077865 DOI: 10.1016/j.cmpb.2021.106190

Cardiovascular disease (CVD) is the leading cause of death worldwide and is a global health issue. Traditionally, statistical models are used commonly in the risk prediction and assessment of CVD. However, the adoption of artificial intelligent (AI) approach is rapidly taking hold in the current era of technology to evaluate patient risks and predict the outcome of CVD. In this review, we outline various conventional risk scores and prediction models and do a comparison with the AI approach. The strengths and limitations of both conventional and AI approaches are discussed. Besides that, biomarker discovery related to CVD are also elucidated as the biomarkers can be used in the risk stratification as well as early detection of the disease. Moreover, problems and challenges involved in current CVD studies are explored. Lastly, future prospects of CVD risk prediction and assessment in the multi-modality of big data integrative approaches are proposed.

Matched MeSH terms: Artificial Intelligence
Fulltext A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic

Rahman MM, Khatun F, Uzzaman A, Sami SI, Bhuiyan MA, Kiong TS

Int J Health Serv, 2021 10;51(4):446-461.
PMID: 33999732 DOI: 10.1177/00207314211017469

The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic's dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.

Matched MeSH terms: Artificial Intelligence
Fulltext Kernel partial diagnostic robust potential to handle high-dimensional and irregular data space on near infrared spectral data

Silalahi DD, Midi H, Arasan J, Mustafa MS, Caliman JP

Heliyon, 2020 Jan;6(1):e03176.
PMID: 32042959 DOI: 10.1016/j.heliyon.2020.e03176

In practice, the collected spectra are very often composes of complex overtone and many overlapping peaks which may lead to misinterpretation because of its significant nonlinear characteristics. Using linear solution might not be appropriate. In addition, with a high-dimension of dataset due to large number of observations and data points the classical multiple regressions will neglect to fit. These complexities commonly will impact to multicollinearity problem, furthermore the risk of contamination of multiple outliers and high leverage points also increases. To address these problems, a new method called Kernel Partial Diagnostic Robust Potential (KPDRGP) is introduced. The method allows the nonlinear solution which maps nonlinearly the original input

X

matrix into higher dimensional feature mapping with corresponds to the Reproducing Kernel Hilbert Spaces (RKHS). In dimensional reduction, the method replaces the dot products calculation of elements in the mapped data to a nonlinear function in the original input space. To prevent the contamination of the multiple outlier and high leverage points the robust procedure using Diagnostic Robust Generalized Potentials (DRGP) algorithm was used. The results verified that using the simulation and real data, the proposed KPDRGP method was superior to the methods in the class of non-kernel and some other robust methods with kernel solution.

Matched MeSH terms: Artificial Intelligence
Prospects and Challenges of MXenes as Emerging Sensing Materials for Flexible and Wearable Breath-Based Biomarker Diagnosis

Hermawan A, Amrillah T, Riapanitra A, Ong WJ, Yin S

Adv Healthc Mater, 2021 10;10(20):e2100970.
PMID: 34318999 DOI: 10.1002/adhm.202100970

A fully integrated, flexible, and functional sensing device for exhaled breath analysis drastically transforms conventional medical diagnosis to non-invasive, low-cost, real-time, and personalized health care. 2D materials based on MXenes offer multiple advantages for accurately detecting various breath biomarkers compared to conventional semiconducting oxides. High surface sensitivity, large surface-to-weight ratio, room temperature detection, and easy-to-assemble structures are vital parameters for such sensing devices in which MXenes have demonstrated all these properties both experimentally and theoretically. So far, MXenes-based flexible sensor is successfully fabricated at a lab-scale and is predicted to be translated into clinical practice within the next few years. This review presents a potential application of MXenes as emerging materials for flexible and wearable sensor devices. The biomarkers from exhaled breath are described first, with emphasis on metabolic processes and diseases indicated by abnormal biomarkers. Then, biomarkers sensing performances provided by MXenes families and the enhancement strategies are discussed. The method of fabrications toward MXenes integration into various flexible substrates is summarized. Finally, the fundamental challenges and prospects, including portable integration with Internet-of-Thing (IoT) and Artificial Intelligence (AI), are addressed to realize marketization.

Matched MeSH terms: Artificial Intelligence
Effectiveness of Create ML in microscopy image classifications: a simple and inexpensive deep learning pipeline for non-data scientists

Nagaki K, Furuta T, Yamaji N, Kuniyoshi D, Ishihara M, Kishima Y, et al.

Chromosome Res, 2021 12;29(3-4):361-371.
PMID: 34648121 DOI: 10.1007/s10577-021-09676-z

Observing chromosomes is a time-consuming and labor-intensive process, and chromosomes have been analyzed manually for many years. In the last decade, automated acquisition systems for microscopic images have advanced dramatically due to advances in their controlling computer systems, and nowadays, it is possible to automatically acquire sets of tiling-images consisting of large number, more than 1000, of images from large areas of specimens. However, there has been no simple and inexpensive system to efficiently select images containing mitotic cells among these images. In this paper, a classification system of chromosomal images by deep learning artificial intelligence (AI) that can be easily handled by non-data scientists was applied. With this system, models suitable for our own samples could be easily built on a Macintosh computer with Create ML. As examples, models constructed by learning using chromosome images derived from various plant species were able to classify images containing mitotic cells among samples from plant species not used for learning in addition to samples from the species used. The system also worked for cells in tissue sections and tetrads. Since this system is inexpensive and can be easily trained via deep learning using scientists' own samples, it can be used not only for chromosomal image analysis but also for analysis of other biology-related images.

Matched MeSH terms: Artificial Intelligence

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