Artificial Intelligence vs. Natural Stupidity: Evaluating AI readiness for the Vietnamese Medical Information System

Net zero supply chain performance and industry 4.0 technologies: Past review and present introspective analysis for future research directions

Interest in applying Industry 4.0 technologies in supply chain operations has increased significantly due to the urgent need to combat climate change and achieve net-zero emissions. This study aims to thoroughly comprehend how Industry 4.0 technologies affect the efficiency of net-zero supply chains. To do so, the study systematically reviews the existing research using 68 academic papers that are thematically analysed and classified by potentials associated with Industry 4.0 in the context of net zero supply chain performance. The findings of this systematic literature review highlight the multifaceted role of Industry 4.0 technologies in achieving net-zero supply chain performance. However, the study also identifies challenges related to policy, technology, economy, and markets to harness these technologies effectively. A conceptual framework is proposed to help organizations strategically leverage Industry 4.0 technologies for sustainable supply chain performance. By identifying knowledge gaps, the review provides a roadmap for future research to explore the complex dynamics at the intersection of Industry 4.0 and sustainability. Practically, the study offers valuable insights for supply chain managers and policymakers on the opportunities and challenges associated with adopting Industry 4.0 technologies for sustainable practices. With the goal of achieving net-zero supply chain performance, this paper emphasizes the importance of a holistic, integrated approach to technology adoption and sustainability strategies.

Healthcare in Vietnam: Harnessing Artificial Intelligence and Robotics to Improve Patient Care Outcomes

Healthcare in Vietnam is increasingly utilizing artificial intelligence (AI) and robotics to enhance patient care outcomes. The Vietnamese healthcare sector recognizes the potential of AI and is actively exploring its applications in research and clinical practice. AI technologies, such as text mining and machine learning, can be employed to analyze medical data and improve decision-making processes. Robotics, on the other hand, can support various healthcare tasks, including elderly care, rehabilitation, and surgical interventions. Robotic surgery, specifically, is an innovative form of minimally invasive surgery that aims to improve surgical outcomes and enhance the patient experience. The implementation of AI in emergency and trauma settings is still in its early stages, but there is a growing interest in and recognition of its potential benefits. However, there are challenges that need to be addressed, such as the need for appropriate research and training programs to support the adoption and integration of AI in healthcare. Despite these challenges, healthcare professionals in Vietnam are optimistic about the potential of AI to improve acute care surgery and are open to embracing new digital technologies. The use of AI and robotics in healthcare aligns with the broader goal of improving healthcare systems in low- and middle-income countries, including Vietnam, through technological advancements. Overall, AI can play an important role in assisting prognosis and predictive analysis by integrating vast amounts of data. Moreover, the integration of AI and robotics in healthcare in Vietnam has the potential to enhance patient care outcomes, improve decision-making processes, and support healthcare professionals in their practice.

Learning health systems in low-income and middle-income countries: exploring evidence and expert insights

Introduction

Learning health systems (LHS) is a multifaceted subject. This paper reviewed current concepts as well as real-world experiences of LHS, drawing on published and unpublished knowledge in order to identify and describe important principles and practices that characterise LHS in low/middle-income country (LMIC) settings.

Methods

We adopted an exploratory approach to the literature review, recognising there are limited studies that focus specifically on system-wide learning in LMICs, but a vast set of connected bodies of literature. 116 studies were included, drawn from an electronic literature search of published and grey literature. In addition, 17 interviews were conducted with health policy and research experts to gain experiential knowledge.

Results

The findings were structured by eight domains on learning enablers. All of these interact with one another and influence actors from community to international levels.

We found that learning comes from the connection between information, deliberation, and action. Moreover, these processes occur at different levels. It is therefore important to consider experiential knowledge from multiple levels and experiences. Creating spaces and providing resources for communities, staff and managers to deliberate on their challenges and find solutions has political implications, however, and is challenging, particularly when resources are constrained, funding and accountability are fragmented and the focus is short-term and narrow. Nevertheless, we can learn from countries that have managed to develop institutional mechanisms and human capacities which help health systems respond to changing environments with ‘best fit’ solutions.

Conclusion

Health systems are knowledge producers, but learning is not automatic. It needs to be valued and facilitated. Everyday governance of health systems can create spaces for reflective practice and learning within routine processes at different levels. This article highlights important enablers, but there remains much work to be done on developing this field of knowledge.

Digital Health Policy and Programs for Hospital Care in Vietnam: Scoping Review

Background

There are a host of emergent technologies with the potential to improve hospital care in low- and middle-income countries such as Vietnam. Wearable monitors and artificial intelligence–based decision support systems could be integrated with hospital-based digital health systems such as electronic health records (EHRs) to provide higher level care at a relatively low cost. However, the appropriate and sustainable application of these innovations in low- and middle-income countries requires an understanding of the local government’s requirements and regulations such as technology specifications, cybersecurity, data-sharing protocols, and interoperability.

Objective

This scoping review aims to explore the current state of digital health research and the policies that govern the adoption of digital health systems in Vietnamese hospitals.

Methods

We conducted a scoping review using a modification of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed and Web of Science were searched for academic publications, and Thư Viện Pháp Luật, a proprietary database of Vietnamese government documents, and the Vietnam Electronic Health Administration website were searched for government documents. Google Scholar and Google Search were used for snowballing searches. The sources were assessed against predefined eligibility criteria through title, abstract, and full-text screening. Relevant information from the included sources was charted and summarized. The review process was primarily undertaken by one researcher and reviewed by another researcher during each step.

Results

In total, 11 academic publications and 20 government documents were included in this review. Among the academic studies, 5 reported engineering solutions for information systems in hospitals, 2 assessed readiness for EHR implementation, 1 tested physicians’ performance before and after using clinical decision support software, 1 reported a national laboratory information management system, and 2 reviewed the health system’s capability to implement eHealth and artificial intelligence. Of the 20 government documents, 19 were promulgated from 2013 to 2020. These regulations and guidance cover a wide range of digital health domains, including hospital information management systems, general and interoperability standards, cybersecurity in health organizations, conditions for the provision of health information technology (HIT), electronic health insurance claims, laboratory information systems, HIT maturity, digital health strategies, electronic medical records, EHRs, and eHealth architectural frameworks.

Conclusions

Research about hospital-based digital health systems in Vietnam is very limited, particularly implementation studies. Government regulations and guidance for HIT in health care organizations have been released with increasing frequency since 2013, targeting a variety of information systems such as electronic medical records, EHRs, and laboratory information systems. In general, these policies were focused on the basic specifications and standards that digital health systems need to meet. More research is needed in the future to guide the implementation of digital health care systems in the Vietnam hospital setting.

Applications of Artificial Intelligence in Non-cardiac Vascular Diseases: A Bibliographic Analysis

Research output related to artificial intelligence (AI) in vascular diseases has been poorly investigated. The aim of this study was to evaluate scientific publications on AI in non-cardiac vascular diseases. A systematic literature search was conducted using the PubMed database and a combination of keywords and focused on three main vascular diseases (carotid, aortic and peripheral artery diseases). Original articles written in English and published between January 1995 and December 2020 were included. Data extracted included the date of publication, the journal, the identity, number, affiliated country of authors, the topics of research, and the fields of AI. Among 171 articles included, the three most productive countries were USA, China, and United Kingdom. The fields developed within AI included: machine learning (n = 90; 45.0%), vision (n = 45; 22.5%), robotics (n = 42; 21.0%), expert system (n = 15; 7.5%), and natural language processing (n = 8; 4.0%). The applications were mainly new tools for: the treatment (n = 52; 29.1%), prognosis (n = 45; 25.1%), the diagnosis and classification of vascular diseases (n = 38; 21.2%), and imaging segmentation (n = 38; 21.2%). By identifying the main techniques and applications, this study also pointed to the current limitations and may help to better foresee future applications for clinical practice.

Determinants of Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective Physicians

Background: This study aimed to develop a theoretical model to explore the behavioral intentions of medical students to adopt an AI-based Diagnosis Support System.

Methods: This online cross-sectional survey used the unified theory of user acceptance of technology (UTAUT) to examine the intentions to use an AI-based Diagnosis Support System in 211 undergraduate medical students in Vietnam. Partial least squares (PLS) structural equational modeling was employed to assess the relationship between latent constructs.

Results: Effort expectancy (β = 0.201, p < 0.05) and social influence (β = 0.574, p < 0.05) were positively associated with initial trust, while no association was found between performance expectancy and initial trust (p > 0.05). Only social influence (β = 0.527, p < 0.05) was positively related to the behavioral intention.

Conclusions: This study highlights positive behavioral intentions in using an AI-based diagnosis support system among prospective Vietnamese physicians, as well as the effect of social influence on this choice. The development of AI-based competent curricula should be considered when reforming medical education in Vietnam.

Application Value of a Deep Convolutional Neural Network Model for Cytological Assessment of Thyroid Nodules

Objective

To investigate the application value of a deep convolutional neural network (CNN) model for cytological assessment of thyroid nodules.

Methods

117 patients with thyroid nodules who underwent thyroid cytology examination in the Affiliated People's Hospital of Ningbo University between January 2017 and December 2019 were included in this study. 100 papillary thyroid cancer samples and 100 nonmalignant samples were collected respectively. The sample images were translated vertically and horizontally. Thus, 900 images were separately created in the vertical and horizontal directions. The sample images were randomly divided into training samples (n = 1260) and test samples (n = 540) at the ratio of 7 : 3 per the training sample to test sample. According to the training samples, the pretrained deep convolutional neural network architecture Resnet50 was trained and fine-tuned. A convolutional neural network-based computer-aided detection (CNN-CAD) system was constructed to perform full-length scan of the test sample slices. The ability of CNN-CAD to screen malignant tumors was analyzed using the threshold setting method. Eighty pathological images were collected from patients who received treatment between January 2020 and May 2020 and used to verify the value of CNN in the screening of malignant thyroid nodules as verification set.

Results

With the number of iterations increasing, the training and verification loss of CNN model gradually decreased and tended to be stable, and the training and verification accuracy of CNN model gradually increased and tended to be stable. The average loss rate of training samples determined by the CNN model was (22.35 ± 0.62) %, and the average loss rate of test samples determined by the CNN model was (26.41 ± 3.37) %. The average accuracy rate of training samples determined by the CNN model was (91.04 ± 2.11) %, and the average accuracy rate of test samples determined by the CNN model was (91.26 ± 1.02)%.

Conclusion

A CNN model exhibits a high value in the cytological diagnosis of thyroid diseases which can be used for the cytological diagnosis of malignant thyroid tumor in the clinic.

The Contribution of Digital Health in the Response to Covid-19 in Vietnam

Emerging from early of 2020, the COVID-19 pandemic has become one of the most serious health crisis globally. In response to such threat, a wide range of digital health applications has been deployed in Vietnam to strengthen surveillance, risk communication, diagnosis, and treatment of COVID-19. Digital health has brought enormous benefits to the fight against COVID-19, however, numerous constrains in digital health application remain. Lack of strong governance of digital health development and deployment; insufficient infrastructure and staff capacity for digital health application are among the main drawbacks. Despite several outstanding problems, digital health is expected to contribute to reducing the spread, improving the effectiveness of pandemic control, and adding to the dramatic transformation of the health system the post-COVID era.

Physician Perception and Satisfaction with Artificial Intelligence in Cancer Treatment: The Watson for Oncology Experience and Implications for Low-Middle Income Countries (Preprint)

As technology continues to improve, health care systems have the opportunity to use a variety of innovative tools for decision-making, including artificial intelligence (AI) applications. However, there has been little research on the feasibility and efficacy of integrating AI systems into real-world clinical practice, especially from the perspectives of clinicians who use such tools. In this paper, we review physicians’ perceptions of and satisfaction with an AI tool, Watson for Oncology, which is used for the treatment of cancer. Watson for Oncology has been implemented in several different settings, including Brazil, China, India, South Korea, and Mexico. By focusing on the implementation of an AI-based clinical decision support system for oncology, we aim to demonstrate how AI can be both beneficial and challenging for cancer management globally and particularly for low-middle–income countries. By doing so, we hope to highlight the need for additional research on user experience and the unique social, cultural, and political barriers to the successful implementation of AI in low-middle–income countries for cancer care.

Towards Microfluidic-Based Exosome Isolation and Detection for Tumor Therapy

Exosomes are a class of cell-secreted, nano-sized extracellular vesicles with a bilayer membrane structure of 30-150 nm in diameter. Their discovery and application have brought breakthroughs in numerous areas, such as liquid biopsies, cancer biology, drug delivery, immunotherapy, tissue repair, and cardiovascular diseases. Isolation of exosomes is the first step in exosome-related research and its applications. Standard benchtop exosome separation and sensing techniques are tedious and challenging, as they require large sample volumes, multi-step operations that are complex and time-consuming, requiring cumbersome and expensive instruments. In contrast, microfluidic platforms have the potential to overcome some of these limitations, owing to their high-precision processing, ability to handle liquids at a microscale, and integrability with various functional units, such as mixers, actuators, reactors, separators, and sensors. These platforms can optimize the detection process on a single device, representing a robust and versatile technique for exosome separation and sensing to attain high purity and high recovery rates with a short processing time. Herein, we overview microfluidic strategies for exosome isolation based on their hydrodynamic properties, size filtration, acoustic fields, immunoaffinity, and dielectrophoretic properties. We focus especially on advances in label-free isolation of exosomes with active biological properties and intact morphological structures. Further, we introduce microfluidic techniques for the detection of exosomal proteins and RNAs with high sensitivity, high specificity, and low detection limits. We summarize the biomedical applications of exosome-mediated therapeutic delivery targeting cancer cells. To highlight the advantages of microfluidic platforms, conventional techniques are included for comparison. Future challenges and prospects of microfluidics towards exosome isolation applications are also discussed. Although the use of exosomes in clinical applications still faces biological, technical, regulatory, and market challenges, in the foreseeable future, recent developments in microfluidic technologies are expected to pave the way for tailoring exosome-related applications in precision medicine.

The Promise of Clinical Decision Support Systems Targetting Low-Resource Settings

Low-resource clinical settings are plagued by low physician-to-patient ratios and a shortage of high-quality medical expertise and infrastructure. Together, these phenomena lead to over-burdened healthcare systems that under-serve the needs of the community. Alleviating this burden can be undertaken by the introduction of clinical decision support systems (CDSSs); systems that support stakeholders (ranging from physicians to patients) within the clinical setting in their day-to-day activities. Such systems, which have proven to be effective in the developed world, remain to be under-explored in low-resource settings. This review attempts to summarize the research focused on clinical decision support systems that either target stakeholders within low-resource clinical settings or diseases commonly found in such environments. When categorizing our findings according to disease applications, we find that CDSSs are predominantly focused on dealing with bacterial infections and maternal care, do not leverage deep learning, and have not been evaluated prospectively. Together, these highlight the need for increased research in this domain in order to impact a diverse set of medical conditions and ultimately improve patient outcomes.

What have Vietnamese scholars learned from researching entrepreneurship? A Systematic review

This study reviews the research landscape of entrepreneurship studies done by Vietnamese researchers from 2008 to 2018. A sample size of 111 articles from 108 academic outlets (journals, conferences proceedings, and book chapters) indexed in Web-of-Science and Scopus were extracted on the SSHPA database, then read and systematically classified into 15 topics. A systematic review reveals (i) a high frequency of research on various aspects of management, (ii) a lackluster focus on innovation and creativity in entrepreneurial activities, (iii) and worrisome cultural influences on the level of creativity. Overall, there was evidence of a detachment between the academic community and the entrepreneurial community. The research landscape shows there have not been enough studies done on the following aspects of entrepreneurship: technology application, poverty reduction, network development, internationalization, inter-generational transfer, and sex/gender.

Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence

Computer-aided diagnosis systems have been developed to assist doctors in diagnosing thyroid nodules to reduce errors made by traditional diagnosis methods, which are mainly based on the experiences of doctors. Therefore, the performance of such systems plays an important role in enhancing the quality of a diagnosing task. Although there have been the state-of-the art studies regarding this problem, which are based on handcrafted features, deep features, or the combination of the two, their performances are still limited. To overcome these problems, we propose an ultrasound image-based diagnosis of the malignant thyroid nodule method using artificial intelligence based on the analysis in both spatial and frequency domains. Additionally, we propose the use of weighted binary cross-entropy loss function for the training of deep convolutional neural networks to reduce the effects of unbalanced training samples of the target classes in the training data. Through our experiments with a popular open dataset, namely the thyroid digital image database (TDID), we confirm the superiority of our method compared to the state-of-the-art methods.

Fundamentals in Artificial Intelligence for Vascular Surgeons

Artificial intelligence (AI) corresponds to a broad discipline that aims to design systems, which display properties of human intelligence. While it has led to many advances and applications in daily life, its introduction in medicine is still in its infancy. AI has created interesting perspectives for medical research and clinical practice but has been sometimes associated with hype leading to a misunderstanding of its real capabilities. Here, we aim to introduce the fundamental notions of AI and to bring an overview of its potential applications for medical and surgical practice. In the limelight of current knowledge, limits and challenges to face as well as future directions are discussed.

Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains

Image-based computer-aided diagnosis (CAD) systems have been developed to assist doctors in the diagnosis of thyroid cancer using ultrasound thyroid images. However, the performance of these systems is strongly dependent on the selection of detection and classification methods. Although there are previous researches on this topic, there is still room for enhancement of the classification accuracy of the existing methods. To address this issue, we propose an artificial intelligence-based method for enhancing the performance of the thyroid nodule classification system. Thus, we extract image features from ultrasound thyroid images in two domains: spatial domain based on deep learning, and frequency domain based on Fast Fourier transform (FFT). Using the extracted features, we perform a cascade classifier scheme for classifying the input thyroid images into either benign (negative) or malign (positive) cases. Through expensive experiments using a public dataset, the thyroid digital image database (TDID) dataset, we show that our proposed method outperforms the state-of-the-art methods and produces up-to-date classification results for the thyroid nodule classification problem.

Artificial Intelligence-Based Classification of Multiple Gastrointestinal Diseases Using Endoscopy Videos for Clinical Diagnosis

Various techniques using artificial intelligence (AI) have resulted in a significant contribution to field of medical image and video-based diagnoses, such as radiology, pathology, and endoscopy, including the classification of gastrointestinal (GI) diseases. Most previous studies on the classification of GI diseases use only spatial features, which demonstrate low performance in the classification of multiple GI diseases. Although there are a few previous studies using temporal features based on a three-dimensional convolutional neural network, only a specific part of the GI tract was involved with the limited number of classes. To overcome these problems, we propose a comprehensive AI-based framework for the classification of multiple GI diseases by using endoscopic videos, which can simultaneously extract both spatial and temporal features to achieve better classification performance. Two different residual networks and a long short-term memory model are integrated in a cascaded mode to extract spatial and temporal features, respectively. Experiments were conducted on a combined dataset consisting of one of the largest endoscopic videos with 52,471 frames. The results demonstrate the effectiveness of the proposed classification framework for multi-GI diseases. The experimental results of the proposed model (97.057% area under the curve) demonstrate superior performance over the state-of-the-art methods and indicate its potential for clinical applications.

Extended Technology Acceptance Model to Predict Mobile-Based Money Acceptance and Sustainability: A Multi-Analytical Structural Equation Modeling and Neural Network Approach

This research is a pioneering study into the adoption of mobile-based money services for financial inclusion and sustainability in developing countries like Togo. Owing to their differences from more usual mobile-based banking and payment services, such technology is being aggressively promoted by providers of network telecommunication companies. However, the factors influencing its sustainable acceptance remain largely unknown. This paper extends the original Technology Acceptance Model (TAM), by integrating self-efficacy (SEMM), technology anxiety (TAMM), and personal innovativeness (PIMM). The research model is assessed with survey data of 539 actual and prospective mobile money users employing structural equation modeling–artificial neural networks (SEM–ANN) approach. A feed-forward-back-propagation (FFBP) multi-layer perceptron (MLP) ANN with significant predictors obtained from SEM as the input units and the root mean square of errors (RMSE) indicated that the ANN method achieves high prediction accuracy. The results present conclusive evidence that perceived ease-of-use (PEMM) is the most significant factor affecting consumers’ attitudes to mobile-based money. While perceived usefulness (PUMM) and PIMM affect adoption decisions, their impact is much lower. Consumer attitudes and intentions were found to have a significant relationship with TAM. SEMM and TAMM; however, they showed mixed results. These findings will be useful to retain prevailing users and attract new ones.

Determinants of Vietnamese Listed Firm Performance: Competition, Wage, CEO, Firm Size, Age, and International Trade

This study investigates the relationship between firms’ competition, wage, CEOs’ characteristics, and firm performance (measured by net income per employee, return on assets (ROA) and return on equity (ROE)) of Vietnam’s 693 listed firms in 2015 using both the ordinary-least-square (OLS) and quantile regression methods. Triangulating the results coming from the analysis of three different measures of firm performance, this study consistently confirms that the sex of CEOs and chairman turns out to be insignificant in explaining firm performance and there is a negative association between capital intensity and firm performance. For financial firms, the age of a firm and average wage per employee are negatively associated with all types of firm performance. The quantile regression method shows that the age of a firm is negatively correlated with its net income per employee for small firms, while it is insignificant for medium-sized firms. Meanwhile, firm size is positively associated with firm performance. These results indicate Vietnam’s business activities are still concentrating on low labor cost, labor intensive, and low-tech production, thus, policies that promote innovation and high-tech applications should be encouraged.

Modeling the Pathways of Knowledge Management Towards Social and Economic Outcomes of Health Organizations

Despite the increasing emphasis placed on knowledge management (KM) by the business sector and the common belief that creating, acquiring, sharing, and the use of knowledge enable individuals, teams, and communities to achieve superior performance, within the healthcare context, there is still room from improvements from both the theoretical and empirical perspectives. The purpose of this paper is to outline the contribution of KM process to the social- and economic-related outcomes in the context of health organizations. Given the theoretical approach on the considered concepts and their relationships, a conceptual model and seven research hypotheses were proposed. The empirical data were provided by a cross-sectional investigation including 459 medical and nonmedical employees of Romanian heath organizations, selected by a mixed method sampling procedure. A partial least squares structural equation modeling (PLS-SEM) approach was selected to provide information on the relevance and significance of the first- and second-order constructs, test the hypotheses, and conduct an importance performance matrix analysis. The PLS-SEM estimation showed positive and significant relationships between KM process and quality of healthcare, and organizational-level social and economic outcomes. Moreover, the research results provided evidences for the complex complementary mediation of the quality of healthcare and social-related outcomes on the relationships between KM process and social and economic outcomes. The theoretical and managerial implications are discussed and suggestions for future research are provided at the end of the paper.