Informatics

19 pages, 1733 KiB

Open AccessArticle

Utilizing LSTM-GRU for IOT-Based Water Level Prediction Using Multi-Variable Rainfall Time Series Data

by Indrastanti Ratna Widiasari and Rissal Efendi

Informatics 2024, 11(4), 73; https://doi.org/10.3390/informatics11040073 - 8 Oct 2024

This research describes experiments using LSTM, GRU models, and a combination of both to predict floods in Semarang based on time series data. The results show that the LSTM model is superior in capturing long-term dependencies, while GRU is better in processing short-term [...] Read more.

This research describes experiments using LSTM, GRU models, and a combination of both to predict floods in Semarang based on time series data. The results show that the LSTM model is superior in capturing long-term dependencies, while GRU is better in processing short-term patterns. By combining the strengths of both models, this hybrid approach achieves better accuracy and robustness in flood prediction. The LSTM-GRU hybrid model outperforms the individual models, providing a more reliable prediction framework. This performance improvement is due to the complementary strengths of LSTM and GRU in handling various aspects of time series data. These findings emphasize the potential of advanced neural network models in addressing complex environmental challenges, paving the way for more effective flood management strategies in Semarang. The performance graph of the LSTM, GRU, and LSTM-GRU models in various scenarios shows significant differences in the performance of predicting river water levels based on rainfall input. The MAPE, MSE, RMSE, and MAD metrics are presented for training and validation data in six scenarios. Overall, the GRU model and the LSTM-GRU combination provide good performance when using more complete input variables, namely, downstream and upstream rainfall, compared to only using downstream rainfall. Full article

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20 pages, 4665 KiB

Open AccessArticle

Using Artificial Intelligence-Based Tools to Improve the Literature Review Process: Pilot Test with the Topic “Hybrid Meat Products”

by Juana Fernández-López, Fernando Borrás-Rocher, Manuel Viuda-Martos and José Ángel Pérez-Álvarez

Informatics 2024, 11(4), 72; https://doi.org/10.3390/informatics11040072 - 5 Oct 2024

Abstract

Conducting a literature review is a mandatory initial stage in scientific research on a specific topic. However, this task is becoming much more complicated in certain areas (such as food science and technology) due to the huge increase in the number of scientific [...] Read more.

Conducting a literature review is a mandatory initial stage in scientific research on a specific topic. However, this task is becoming much more complicated in certain areas (such as food science and technology) due to the huge increase in the number of scientific publications. Different tools based on artificial intelligence could be very useful for this purpose. This paper addresses this challenge by developing and checking different tools applicated to an emerging topic in food science and technology: “hybrid meat products”. The first tool to be applied was based on Natural Language Processing and was used to select and reduce the initial number of papers obtained from a traditional bibliographic search (using common scientific databases such as Web Science and Scopus) from 938 to 178 (a 87% reduction). The second tool was a project based on the interplay between Retrieval-Augmented Generation (RAG) and LLAMA 3, which was used to answer key questions relating to the topic under review (“hybrid meat products”) but limiting the context to the scientific review obtained after applying the first AI tool. This new strategy for reviewing scientific literature could be a major advance on from the traditional literature review procedure, making it faster, more open, more accessible to everyone, more effective, more objective, and more efficient—all of which help to fulfill the principles of open science. Full article

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22 pages, 3147 KiB

Open AccessReview

Edge Computing and Cloud Computing for Internet of Things: A Review

by Francesco Cosimo Andriulo, Marco Fiore, Marina Mongiello, Emanuele Traversa and Vera Zizzo

Informatics 2024, 11(4), 71; https://doi.org/10.3390/informatics11040071 - 30 Sep 2024

Abstract

The rapid expansion of the Internet of Things ecosystem has created an urgent need for efficient data processing and analysis technologies. This review aims to systematically examine and compare edge computing, cloud computing, and hybrid architectures, focusing on their applications within IoT environments. [...] Read more.

The rapid expansion of the Internet of Things ecosystem has created an urgent need for efficient data processing and analysis technologies. This review aims to systematically examine and compare edge computing, cloud computing, and hybrid architectures, focusing on their applications within IoT environments. The methodology involved a comprehensive search and analysis of peer-reviewed journals, conference proceedings, and industry reports, highlighting recent advancements in computing technologies for IoT. Key findings reveal that edge computing excels in reducing latency and enhancing data privacy through localized processing, while cloud computing offers superior scalability and flexibility. Hybrid approaches, such as fog and mist computing, present a promising solution by combining the strengths of both edge and cloud systems. These hybrid models optimize bandwidth use and support low-latency, privacy-sensitive applications in IoT ecosystems. Hybrid architectures are identified as particularly effective for scenarios requiring efficient bandwidth management and low-latency processing. These models represent a significant step forward in addressing the limitations of both edge and cloud computing for IoT, offering a balanced approach to data analysis and resource management. Full article

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24 pages, 2469 KiB

Open AccessReview

by Panagiotis D. Petridis, Aleksandra S. Kristo, Angelos K. Sikalidis and Ilias K. Kitsas

Informatics 2024, 11(4), 70; https://doi.org/10.3390/informatics11040070 - 27 Sep 2024

Abstract

Type 2 diabetes mellitus (T2DM) is a chronic disease characterized by elevated blood glucose levels and insulin resistance, leading to multiple organ damage with implications for quality of life and lifespan. In recent years, the rising prevalence of T2DM globally has coincided with [...] Read more.

Type 2 diabetes mellitus (T2DM) is a chronic disease characterized by elevated blood glucose levels and insulin resistance, leading to multiple organ damage with implications for quality of life and lifespan. In recent years, the rising prevalence of T2DM globally has coincided with the digital transformation of medicine and healthcare, including extensive electronic health records (EHRs) for patients and healthy individuals. Numerous research articles as well as systematic reviews have been conducted to produce innovative findings and summarize current developments and applications of data science in the life sciences, medicine and healthcare. The present review is conducted in the context of T2DM and Machine Learning, examining relatively recent publications using tabular data and demonstrating the relevant use cases, the workflows during model building and the candidate predictors. Our work indicates that Gradient Boosting and tree-based models are the most successful ones, the SHAPley and Wrapper algorithms being quite popular feature interpretation and evaluation methods, highlighting urinary markers and dietary intake as emerging diabetes predictors besides the typical invasive ones. These results could offer insight toward better management of diabetes and open new avenues for research. Full article

(This article belongs to the Section Machine Learning)

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20 pages, 5363 KiB

Open AccessArticle

Differential Classification of Dengue, Zika, and Chikungunya Using Machine Learning—Random Forest and Decision Tree Techniques

by Wilson Arrubla-Hoyos, Jorge Gómez Gómez and Emiro De-La-Hoz-Franco

Informatics 2024, 11(3), 69; https://doi.org/10.3390/informatics11030069 - 20 Sep 2024

Abstract

Dengue, Zika, and chikungunya viruses pose a serious threat globally and circulate widely in America. These diseases share similar symptoms in their early stages, which can make early diagnosis difficult. In this study, two predictive models based on Decision Trees and Random Forests [...] Read more.

Dengue, Zika, and chikungunya viruses pose a serious threat globally and circulate widely in America. These diseases share similar symptoms in their early stages, which can make early diagnosis difficult. In this study, two predictive models based on Decision Trees and Random Forests were developed to classify dengue, Zika, and chikungunya, with the aim of being supportive and easily interpretable for the medical community. To achieve this, a dataset was collected from a clinic in Sincelejo, Colombia, including the signs, symptoms, and laboratory results of these diseases. The Pan American Health Organization (PAHO) Diagnostic Guide 2022 methodology for the differential classification of dengue and chikungunya was applied by assigning evaluative weights to symptoms in the dataset. In addition, a bootstrapping resampling technique based on the central limit theorem was used to balance the target variable, and cross-validation was used to train the models. The main results were obtained with the Random Forest technique, achieving an accuracy of 99.7% for classifying chikungunya, 99.1% for dengue, and 98.8% for Zika. This study represents a significant advance in the differential prediction of these diseases through the use of automatic learning techniques and the integration of clinical and laboratory information. Full article

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37 pages, 8423 KiB

Open AccessArticle

Enhancing Clinical Decision Support for Precision Medicine: A Data-Driven Approach

by Nasim Sadat Mosavi and Manuel Filipe Santos

Informatics 2024, 11(3), 68; https://doi.org/10.3390/informatics11030068 - 13 Sep 2024

Abstract

Precision medicine has emerged as a transformative approach aimed at tailoring treatment to individual patients, moving away from the traditional one-size-fits-all model. However, Clinical decision support systems encounter challenges, particularly in terms of data aspects. In response, our study proposes a data-driven framework [...] Read more.

Precision medicine has emerged as a transformative approach aimed at tailoring treatment to individual patients, moving away from the traditional one-size-fits-all model. However, Clinical decision support systems encounter challenges, particularly in terms of data aspects. In response, our study proposes a data-driven framework rooted in Simon’s decision-making model. This framework leverages advanced technologies such as artificial intelligence and data analytics to enhance clinical decision-making in precision medicine. By addressing limitations and integrating AI and analytics, our study contributes to the advancement of optimal clinical decision-making practices in precision healthcare. Full article

(This article belongs to the Section Medical and Clinical Informatics)

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20 pages, 5827 KiB

Open AccessArticle

Pruning Policy for Image Classification Problems Based on Deep Learning

by Cesar G. Pachon, Javier O. Pinzon-Arenas and Dora Ballesteros

Informatics 2024, 11(3), 67; https://doi.org/10.3390/informatics11030067 - 12 Sep 2024

Abstract

In recent years, several methods have emerged for compressing image classification models using CNNs, for example, by applying pruning to the convolutional layers of the network. Typically, each pruning method uses a type of pruning distribution that is not necessarily the most appropriate [...] Read more.

In recent years, several methods have emerged for compressing image classification models using CNNs, for example, by applying pruning to the convolutional layers of the network. Typically, each pruning method uses a type of pruning distribution that is not necessarily the most appropriate for a given classification problem. Therefore, this paper proposes a methodology to select the best pruning policy (method + pruning distribution) for a specific classification problem and global pruning rate to obtain the best performance of the compressed model. This methodology was applied to several image datasets to show the influence not only of the method but also of the pruning distribution on the quality of the pruned model. It was shown that the selected pruning policy affects the performance of the pruned model to different extents, and that it depends on the classification problem to be addressed. For example, while for the Date Fruit Dataset, variations of more than 10% were obtained, for CIFAR10, variations were less than 5% for the same cases evaluated. Full article

(This article belongs to the Section Machine Learning)

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23 pages, 3745 KiB

Open AccessArticle

Language Differences in Online Complaint Responses between Generative Artificial Intelligence and Hotel Managers

by Yau-Ni Wan

Informatics 2024, 11(3), 66; https://doi.org/10.3390/informatics11030066 - 5 Sep 2024

Abstract

Since November 2022, the use of generative artificial intelligence (GAI) technology has increased in many customer service industries. However, little is known about AI’s language choices and meaning-making resources compared to human responses from a systematic linguistic point of view. The present study [...] Read more.

Since November 2022, the use of generative artificial intelligence (GAI) technology has increased in many customer service industries. However, little is known about AI’s language choices and meaning-making resources compared to human responses from a systematic linguistic point of view. The present study is a discourse analysis that explores negative online guest complaints made to four luxury heritage hotels in Hong Kong that are classified as cultural heritage sites with rich interpersonal and historical values. We collected authentic guest complaints and responses from hotel managers from April 2012 to October 2022 in online travel forums, and then had GAI draft response letters on behalf of the hotel managers. Our total dataset was 65,539 words and consisted of three subcorpora: guest complaints (Text a of 115 complaints totaling 26,224 words), hotel manager responses (Text b of 115 response letters totaling 14,975 words), and AI-generated responses (Text c of 115 response letters totaling 24,340 words). This study used systemic functional linguistics to explore interpersonal meanings in texts; for example, appraisal resources, verb processes, and personal pronouns were compared between texts. First, we identified the most frequent words of the common themes across the three subcorpora and found significant differences in lexicogrammatical features between hotel managers and AI-generated responses using the log-likelihood ratio. The results suggest that AI-generated texts are able to provide a tailored and empathetic response to guests, but hotel managers may need to introduce some modifications, such as time indicators, sensory verbs used, and complimentary offers. This study explores the differences in word choices and communication strategies, which have implications and insights for the hospitality industry, especially luxury heritage hotels where caring and personalized customer service are considered important. Full article

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10 pages, 3784 KiB

Open AccessArticle

Adopting Business Intelligence Techniques in Healthcare Practice

by Hui-Chuan Huang, Hui-Kuan Wang, Hwei-Ling Chen, Jeng Wei, Wei-Hsian Yin and Kuan-Chia Lin

Informatics 2024, 11(3), 65; https://doi.org/10.3390/informatics11030065 - 4 Sep 2024

Abstract

With the rapid development of information technology, digital health technologies have become increasingly prevalent in the field of healthcare. In this study, business intelligence (BI) techniques were combined with research-based prediction models to increase the efficiency and quality of healthcare practices. A data [...] Read more.

With the rapid development of information technology, digital health technologies have become increasingly prevalent in the field of healthcare. In this study, business intelligence (BI) techniques were combined with research-based prediction models to increase the efficiency and quality of healthcare practices. A data scenario involving 200 older adults with various measurements, including health beliefs, social support, self-efficacy, and disease duration, was used to establish a medication adherence prediction model in a BI system. A regression model, logistic regression model, tree model, and score-based prediction model were used to predict medication adherence among older adults. The developed BI-based prediction model has visualization, real-time feedback, and data updating functionality. These features enhanced the effectiveness of prediction models in clinical practice. Healthcare professionals can incorporate the proposed system into their care practice for health assessments and management, and patients can use the system to manage themselves. The developed BI-based care system can also be used to achieve effective communication and shared decision-making between care managers and patients. Further empirical studies integrating prediction models into the proposed BI system for assessment, management, and decision-making in healthcare practice are warranted. Full article

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28 pages, 2466 KiB

Open AccessReview

Navigating Governmental Choices: A Comprehensive Review of Artificial Intelligence’s Impact on Decision-Making

by Gustavo Caiza, Verónica Sanguña, Natalia Tusa, Violeta Masaquiza, Alexandra Ortiz and Marcelo V. Garcia

Informatics 2024, 11(3), 64; https://doi.org/10.3390/informatics11030064 - 4 Sep 2024

Abstract

The integration of artificial intelligence (AI) into government decision-making is rapidly gaining traction in public administration and politics. This scoping review, guided by PRISMA protocols, examines 50 articles from reputable sources like Scopus and SpringerLink to analyze the trends, benefits, and challenges of [...] Read more.

The integration of artificial intelligence (AI) into government decision-making is rapidly gaining traction in public administration and politics. This scoping review, guided by PRISMA protocols, examines 50 articles from reputable sources like Scopus and SpringerLink to analyze the trends, benefits, and challenges of AI in governance. While AI offers substantial potential to enhance government efficiency and service delivery, significant barriers remain, including concerns about bias, transparency, public acceptance, and accountability. This review underscores the need for ongoing research and dialogue on the ethical, social, and practical implications of AI in government to ensure the responsible and inclusive adoption of AI-driven public services. Full article

(This article belongs to the Section Social Informatics and Digital Humanities)

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16 pages, 2212 KiB

Open AccessArticle

Health Benefits and Adverse Effects of Kratom: A Social Media Text-Mining Approach

by Abdullah Wahbeh, Mohammad Al-Ramahi, Omar El-Gayar, Tareq Nasralah and Ahmed Elnoshokaty

Informatics 2024, 11(3), 63; https://doi.org/10.3390/informatics11030063 - 30 Aug 2024

Abstract

Background: Kratom is a substance that alters one’s mental state and is used for pain relief, mood enhancement, and opioid withdrawal, despite potential health risks. In this study, we aim to analyze the social media discourse about kratom to provide more insights about [...] Read more.

Background: Kratom is a substance that alters one’s mental state and is used for pain relief, mood enhancement, and opioid withdrawal, despite potential health risks. In this study, we aim to analyze the social media discourse about kratom to provide more insights about kratom’s benefits and adverse effects. Also, we aim to demonstrate how algorithmic machine learning approaches, qualitative methods, and data visualization techniques can complement each other to discern diverse reactions to kratom’s effects, thereby complementing traditional quantitative and qualitative methods. Methods: Social media data were analyzed using the latent Dirichlet allocation (LDA) algorithm, PyLDAVis, and t-distributed stochastic neighbor embedding (t-SNE) technique to identify kratom’s benefits and adverse effects. Results: The analysis showed that kratom aids in addiction recovery and managing opiate withdrawal, alleviates anxiety, depression, and chronic pain, enhances mood, energy, and overall mental well-being, and improves quality of life. Conversely, it may induce nausea, upset stomach, and constipation, elevate heart risks, affect respiratory function, and threaten liver health. Additional reported side effects include brain damage, weight loss, seizures, dry mouth, itchiness, and impacts on sexual function. Conclusion: This combined approach underscores its effectiveness in providing a comprehensive understanding of diverse reactions to kratom, complementing traditional research methodologies used to study kratom. Full article

(This article belongs to the Section Health Informatics)

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27 pages, 1196 KiB

Open AccessReview

Artificial Intelligence and Machine Learning Technologies for Personalized Nutrition: A Review

by Dimitris Tsolakidis, Lazaros P. Gymnopoulos and Kosmas Dimitropoulos

Informatics 2024, 11(3), 62; https://doi.org/10.3390/informatics11030062 - 28 Aug 2024

Abstract

Modern lifestyle trends, such as sedentary behaviour and unhealthy diets, have been associated with obesity, a major health challenge increasing the risk of multiple pathologies. This has prompted many to reassess their routines and seek expert guidance on healthy living. In the digital [...] Read more.

Modern lifestyle trends, such as sedentary behaviour and unhealthy diets, have been associated with obesity, a major health challenge increasing the risk of multiple pathologies. This has prompted many to reassess their routines and seek expert guidance on healthy living. In the digital era, users quickly turn to mobile apps for support. These apps monitor various aspects of daily life, such as physical activity and calorie intake; collect extensive user data; and apply modern data-driven technologies, including artificial intelligence (AI) and machine learning (ML), to provide personalised diet and lifestyle recommendations. This work examines the state of the art in data-driven technologies for personalised nutrition, including relevant data collection technologies, and explores the research challenges in this field. A literature review, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, was conducted using three databases, covering studies from 2021 to 2024, resulting in 67 final studies. The data are presented in separate subsections for recommendation systems (43 works) and data collection technologies (17 works), with a discussion section identifying research challenges. The findings indicate that the fields of data-driven innovation and personalised nutrition are predominately amalgamated in the use of recommender systems. Full article

(This article belongs to the Section Health Informatics)

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27 pages, 4601 KiB

Open AccessArticle

TEADASH: Implementing and Evaluating a Teacher-Facing Dashboard Using Design Science Research

by Ngoc Buu Cat Nguyen, Marcus Lithander, Christian Master Östlund, Thashmee Karunaratne and William Jobe

Informatics 2024, 11(3), 61; https://doi.org/10.3390/informatics11030061 - 26 Aug 2024

Abstract

The benefits of teacher-facing dashboards are incontestable, yet their evidence is finite in terms of long-term use, meaningful usability, and maturity level. Thus, this paper uses design science research and critical theory to design and develop TEADASH to support teachers in making decisions [...] Read more.

The benefits of teacher-facing dashboards are incontestable, yet their evidence is finite in terms of long-term use, meaningful usability, and maturity level. Thus, this paper uses design science research and critical theory to design and develop TEADASH to support teachers in making decisions on teaching and learning. Three cycles of design science research and multiple small loops were implemented to develop the dashboard. The tool was then deployed and evaluated in real time with the authentic courses. Five courses from two Swedish universities were included in this study. The co-design with teachers is crucial to the applicability of this dashboard, while letting teachers use the tool during their courses is more important to help them to recognize the features they actually use and the tool’s usefulness for their teaching practices. TEADASH can address the prior matters, align with the learning design, and meet teachers’ needs. The technical and co-design aspects, as well as the advantages and challenges of applying TEADASH in practice, are also discussed here. Full article

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20 pages, 1568 KiB

Open AccessSystematic Review

Knowledge Management for Improved Digital Transformation in Insurance Companies: Systematic Review and Perspectives

by Younes Elgargouh, Mohammed Reda Chbihi Louhdi, El Moukhtar Zemmouri and Hicham Behja

Informatics 2024, 11(3), 60; https://doi.org/10.3390/informatics11030060 - 12 Aug 2024

Abstract

Knowledge Management (KM) plays a pivotal role in contemporary businesses, facilitating the identification, management, and utilization of existing knowledge for organizational benefit. This article underscores the indispensability of effective KM processes in the insurance industry, which is undergoing profound digital transformation. Through a [...] Read more.

Knowledge Management (KM) plays a pivotal role in contemporary businesses, facilitating the identification, management, and utilization of existing knowledge for organizational benefit. This article underscores the indispensability of effective KM processes in the insurance industry, which is undergoing profound digital transformation. Through a systematic review utilizing the PRISMA framework, we meta-analyzed 85 high-quality scientific papers sourced from prominent databases spanning 2008 to 2022. Our examination centers on the diverse implementation processes of KM worldwide, emphasizing the integration of information technologies to enhance data collection, analysis, processing, and distribution within insurance companies. The objective of this review is twofold: to devise efficient methods for implementing KM systems in the insurance sector and to delineate practical research directions in this domain. Full article

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26 pages, 9128 KiB

Open AccessArticle

AI-Based Visual Early Warning System

by Zeena Al-Tekreeti, Jeronimo Moreno-Cuesta, Maria Isabel Madrigal Garcia and Marcos A. Rodrigues

Informatics 2024, 11(3), 59; https://doi.org/10.3390/informatics11030059 - 12 Aug 2024

Abstract

Facial expressions are a universally recognised means of conveying internal emotional states across diverse human cultural and ethnic groups. Recent advances in understanding people’s emotions expressed through verbal and non-verbal communication are particularly noteworthy in the clinical context for the assessment of patients’ [...] Read more.

Facial expressions are a universally recognised means of conveying internal emotional states across diverse human cultural and ethnic groups. Recent advances in understanding people’s emotions expressed through verbal and non-verbal communication are particularly noteworthy in the clinical context for the assessment of patients’ health and well-being. Facial expression recognition (FER) plays an important and vital role in health care, providing communication with a patient’s feelings and allowing the assessment and monitoring of mental and physical health conditions. This paper shows that automatic machine learning methods can predict health deterioration accurately and robustly, independent of human subjective assessment. The prior work of this paper is to discover the early signs of deteriorating health that align with the principles of preventive reactions, improving health outcomes and human survival, and promoting overall health and well-being. Therefore, methods are developed to create a facial database mimicking the underlying muscular structure of the face, whose Action Unit motions can then be transferred to human face images, thus displaying animated expressions of interest. Then, building and developing an automatic system based on convolution neural networks (CNN) and long short-term memory (LSTM) to recognise patterns of facial expressions with a focus on patients at risk of deterioration in hospital wards. This research presents state-of-the-art results on generating and modelling synthetic database and automated deterioration prediction through FEs with 99.89% accuracy. The main contributions to knowledge from this paper can be summarized as (1) the generation of visual datasets mimicking real-life samples of facial expressions indicating health deterioration, (2) improvement of the understanding and communication with patients at risk of deterioration through facial expression analysis, and (3) development of a state-of-the-art model to recognize such facial expressions using a ConvLSTM model. Full article

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Graphical abstract

29 pages, 456 KiB

Open AccessSystematic Review

Ethical Challenges and Solutions of Generative AI: An Interdisciplinary Perspective

by Mousa Al-kfairy, Dheya Mustafa, Nir Kshetri, Mazen Insiew and Omar Alfandi

Informatics 2024, 11(3), 58; https://doi.org/10.3390/informatics11030058 - 9 Aug 2024

Abstract

This paper conducts a systematic review and interdisciplinary analysis of the ethical challenges of generative AI technologies (N = 37), highlighting significant concerns such as privacy, data protection, copyright infringement, misinformation, biases, and societal inequalities. The ability of generative AI to produce convincing [...] Read more.

This paper conducts a systematic review and interdisciplinary analysis of the ethical challenges of generative AI technologies (N = 37), highlighting significant concerns such as privacy, data protection, copyright infringement, misinformation, biases, and societal inequalities. The ability of generative AI to produce convincing deepfakes and synthetic media, which threaten the foundations of truth, trust, and democratic values, exacerbates these problems. The paper combines perspectives from various disciplines, including education, media, and healthcare, underscoring the need for AI systems that promote equity and do not perpetuate social inequalities. It advocates for a proactive approach to the ethical development of AI, emphasizing the necessity of establishing policies, guidelines, and frameworks that prioritize human rights, fairness, and transparency. The paper calls for a multidisciplinary dialogue among policymakers, technologists, and researchers to ensure responsible AI development that conforms to societal values and ethical standards. It stresses the urgency of addressing these ethical concerns and advocates for the development of generative AI in a socially beneficial and ethically sound manner, contributing significantly to the discourse on managing AI’s ethical implications in the modern digital era. The study highlights the theoretical and practical implications of these challenges and suggests a number of future research directions. Full article

(This article belongs to the Section Social Informatics and Digital Humanities)

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23 pages, 2024 KiB

Open AccessReview

Large Language Models in Healthcare and Medical Domain: A Review

by Zabir Al Nazi and Wei Peng

Informatics 2024, 11(3), 57; https://doi.org/10.3390/informatics11030057 - 7 Aug 2024

Abstract

The deployment of large language models (LLMs) within the healthcare sector has sparked both enthusiasm and apprehension. These models exhibit the remarkable ability to provide proficient responses to free-text queries, demonstrating a nuanced understanding of professional medical knowledge. This comprehensive survey delves into [...] Read more.

The deployment of large language models (LLMs) within the healthcare sector has sparked both enthusiasm and apprehension. These models exhibit the remarkable ability to provide proficient responses to free-text queries, demonstrating a nuanced understanding of professional medical knowledge. This comprehensive survey delves into the functionalities of existing LLMs designed for healthcare applications and elucidates the trajectory of their development, starting with traditional Pretrained Language Models (PLMs) and then moving to the present state of LLMs in the healthcare sector. First, we explore the potential of LLMs to amplify the efficiency and effectiveness of diverse healthcare applications, particularly focusing on clinical language understanding tasks. These tasks encompass a wide spectrum, ranging from named entity recognition and relation extraction to natural language inference, multimodal medical applications, document classification, and question-answering. Additionally, we conduct an extensive comparison of the most recent state-of-the-art LLMs in the healthcare domain, while also assessing the utilization of various open-source LLMs and highlighting their significance in healthcare applications. Furthermore, we present the essential performance metrics employed to evaluate LLMs in the biomedical domain, shedding light on their effectiveness and limitations. Finally, we summarize the prominent challenges and constraints faced by large language models in the healthcare sector by offering a holistic perspective on their potential benefits and shortcomings. This review provides a comprehensive exploration of the current landscape of LLMs in healthcare, addressing their role in transforming medical applications and the areas that warrant further research and development. Full article

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14 pages, 752 KiB

Open AccessArticle

Digital Innovations in E-Commerce: Augmented Reality Applications in Online Fashion Retail—A Qualitative Study among Gen Z Consumers

by Ildikó Kovács and Éva Réka Keresztes

Informatics 2024, 11(3), 56; https://doi.org/10.3390/informatics11030056 - 3 Aug 2024

Abstract

Digital innovations have significantly transformed the marketing landscape, with visual technology solutions having become mainstream in the fashion industry approximately a decade ago. Digital technology offers a range of benefits to online fashion retailers, enhancing their online shopping platforms with augmented reality features [...] Read more.

Digital innovations have significantly transformed the marketing landscape, with visual technology solutions having become mainstream in the fashion industry approximately a decade ago. Digital technology offers a range of benefits to online fashion retailers, enhancing their online shopping platforms with augmented reality features that allow customers to “try on” products digitally before making a purchase. This research aims to explore the key factors influencing the use of augmented reality applications and e-commerce sites for purchasing apparel. A qualitative study was conducted to examine the visual experience and usage of augmented reality applications among young customers. The findings highlight the most relevant factors in the online fashion purchasing process, the visual experience, and the potential future use of augmented reality applications in fashion product purchasing. These insights are crucial for developing effective marketing strategies and communication messages. Full article

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17 pages, 260 KiB

Open AccessArticle

Internet Use for Health-Related Purposes among Older People in Thailand: An Analysis of Nationwide Cross-Sectional Data

by Kittisak Robru, Prasongchai Setthasuravich, Aphisit Pukdeewut and Suthiwat Wetchakama

Informatics 2024, 11(3), 55; https://doi.org/10.3390/informatics11030055 - 28 Jul 2024

Cited by 2

Abstract

As the global population ages, understanding the digital health behaviors of older adults becomes increasingly crucial. In Thailand, where the elderly population is rapidly growing, examining how older individuals use the internet for health-related purposes can provide valuable insights for enhancing healthcare accessibility [...] Read more.

As the global population ages, understanding the digital health behaviors of older adults becomes increasingly crucial. In Thailand, where the elderly population is rapidly growing, examining how older individuals use the internet for health-related purposes can provide valuable insights for enhancing healthcare accessibility and engagement. This study investigates the use of the internet for health-related purposes among older adults in Thailand, focusing on the socio-demographic factors influencing this behavior. Utilizing cross-sectional data from the “Thailand Internet User Behavior Survey 2022”, which includes responses from 4652 older adults, the study employs descriptive statistics, chi-square tests, and logistic regression analysis. The results reveal that approximately 10.83% of older adults use the internet for health purposes. The analysis shows that higher income (AOR = 1.298, p = 0.030), higher level of education (degree education: AOR = 1.814, p < 0.001), skilled occupations (AOR = 2.003, p < 0.001), residence in an urban area (AOR = 3.006, p < 0.001), and greater confidence in internet use (very confident: AOR = 3.153, p < 0.001) are significantly associated with a greater likelihood of using the internet for health purposes. Gender and age did not show significant differences in health-related internet use, indicating a relatively gender-neutral and age-consistent landscape. Significant regional differences were observed, with the northeastern region showing a markedly higher propensity (AOR = 2.249, p < 0.001) for health-related internet use compared to the northern region. Meanwhile, the eastern region (AOR = 0.489, p = 0.018) showed lower odds. These findings underscore the need for targeted healthcare policies to enhance digital health engagement among older adults in Thailand, emphasizing the importance of improving digital literacy, expanding infrastructure, and addressing region-specific health initiatives. Full article

(This article belongs to the Section Health Informatics)

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