IEEE Xplore Research Articles

Automatic distractor generation in multiple-choice questions: a systematic literature review

Background Multiple-choice questions (MCQs) are one of the most used assessment formats. However, creating MCQs is a challenging task, particularly when formulating the distractor. Numerous studies have proposed automatic distractor generation. However, there has been no literature review to summarize and present the current state of research in this field. This study aims to perform a systematic literature review to identify trends and the state of the art of automatic distractor generation studies. Methodology We conducted a systematic literature following the Kitchenham framework. The relevant literature was retrieved from the ACM Digital Library, IEEE Xplore, Science Direct, and Scopus databases. Results A total of 60 relevant studies from 2009 to 2024 were identified and extracted to answer three research questions regarding the data sources, methods, types of questions, evaluation, languages, and domains used in the automatic distractor generation research. The results of the study indicated that automatic distractor generation has been growing with improvement and expansion in many aspects. Furthermore, trends and the state of the art in this topic were observed. Conclusions Nevertheless, we identified potential research gaps, including the need to explore further data sources, methods, languages, and domains. This study can serve as a reference for future studies proposing research within the field of automatic distractor generation.

Predicting adverse drug event using machine learning based on electronic health records: a systematic review and meta-analysis

IntroductionAdverse drug events (ADEs) pose a significant challenge in current clinical practice. Machine learning (ML) has been increasingly used to predict specific ADEs using electronic health record (EHR) data. This systematic review provides a comprehensive overview of the application of ML in predicting specific ADEs based on EHR data.MethodsA systematic search of PubMed, Web of Science, Embase, and IEEE Xplore was conducted to identify relevant articles published from the inception to 20 May 2024. Studies that developed ML models for predicting specific ADEs or ADEs associated with particular drugs were included using EHR data.ResultsA total of 59 studies met the inclusion criteria, covering 15 drugs and 15 ADEs. In total, 38 machine learning algorithms were reported, with random forest (RF) being the most frequently used, followed by support vector machine (SVM), eXtreme gradient boosting (XGBoost), decision tree (DT), and light gradient boosting machine (LightGBM). The performance of the ML models was generally strong, with an average area under the curve (AUC) of 76.68% ± 10.73, accuracy of 76.00% ± 11.26, precision of 60.13% ± 24.81, sensitivity of 62.35% ± 20.19, specificity of 75.13% ± 16.60, and an F1 score of 52.60% ± 21.10. The combined sensitivity, specificity, diagnostic odds ratio (DOR), and AUC from the summary receiver operating characteristic (SROC) curve using a random effects model were 0.65 (95% CI: 0.65–0.66), 0.89 (95% CI: 0.89–0.90), 12.11 (95% CI: 8.17–17.95), and 0.8069, respectively. The risk factors associated with different drugs and ADEs varied.DiscussionFuture research should focus on improving standardization, conducting multicenter studies that incorporate diverse data types, and evaluating the impact of artificial intelligence predictive models in real-world clinical settings.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024565842, identifier CRD42024565842.

Machine learning-based infection diagnostic and prognostic models in post-acute care settings: a systematic review

Abstract Objectives This study aims to (1) review machine learning (ML)-based models for early infection diagnostic and prognosis prediction in post-acute care (PAC) settings, (2) identify key risk predictors influencing infection-related outcomes, and (3) examine the quality and limitations of these models. Materials and Methods PubMed, Web of Science, Scopus, IEEE Xplore, CINAHL, and ACM digital library were searched in February 2024. Eligible studies leveraged PAC data to develop and evaluate ML models for infection-related risks. Data extraction followed the CHARMS checklist. Quality appraisal followed the PROBAST tool. Data synthesis was guided by the socio-ecological conceptual framework. Results Thirteen studies were included, mainly focusing on respiratory infections and nursing homes. Most used regression models with structured electronic health record data. Since 2020, there has been a shift toward advanced ML algorithms and multimodal data, biosensors, and clinical notes being significant sources of unstructured data. Despite these advances, there is insufficient evidence to support performance improvements over traditional models. Individual-level risk predictors, like impaired cognition, declined function, and tachycardia, were commonly used, while contextual-level predictors were barely utilized, consequently limiting model fairness. Major sources of bias included lack of external validation, inadequate model calibration, and insufficient consideration of data complexity. Discussion and Conclusion Despite the growth of advanced modeling approaches in infection-related models in PAC settings, evidence supporting their superiority remains limited. Future research should leverage a socio-ecological lens for predictor selection and model construction, exploring optimal data modalities and ML model usage in PAC, while ensuring rigorous methodologies and fairness considerations.

Abstract 4115654: Assessing Diagnostic Performance: A Systematic Review and Meta-Analysis of Machine Learning-Based Fractional Flow Reserve versus Traditional Invasive Fractional Flow Reserve for Coronary Artery Stenosis Evaluation

Introduction: Fractional Flow Reserve (FFR) is a stenosis-specific index for coronary artery stenosis (CAS) detection and its effect on myocardial perfusion. Visualizing stenosis through an invasive coronary angiography (iFFR) lacks consistency and is highly subjective. Recently, non-invasive techniques have been developed using artificial intelligence (AI) and Machine learning (ML) to measure FFR (ML-FFR). We aimed to analyze the diagnostic performance of ML-FFR in comparison to iFFR in the detection of CAS. Methods: A search was carried out in PubMed, IEEE Xplore digital library, and clinicaltrials.gov where studies evaluating the risk of CAS in patients undergoing ML-FFR and iFFR were considered in terms of FFR values from 2014 to 2024. OpenMetaAnalyst was used to evaluate the diagnostic performance in terms of pooled Sensitivity(SN), Specificity(SP), Negative Likelihood Ratio(NLR), Positive Likelihood Ratio(PLR), and Diagnostic Odds Ratio(DOR) at 95% confidence interval (CI). P<0.05 was considered significant. Results: A total of 9 studies including 1358 vessel lesions were analyzed. The pooled SN, SP, NLR, and PLR were 81.1%(74.3%-86.4,p=0.014), 91.8%(84.9%-95.7%, p<0.001), 0.187 (0.151-0.232, p=0.575) and 9.898(5.492-17.839,p<0.001), respectively with >75% heterogeneity in SP and PLR. After excluding two studies, a “leave-out” analysis showed pooled SN, SP, NLR, and PLR as 79.9% (71.4%-86.3%, p=0.007), 94.4% (90.2%-96.9%, p=0.002), 0.187 ( 0.140-0.250, p=0.540) and 14.307 (8.094-25.289, p=0.004) with <70% heterogeneity. The pooled DOR was 53.42 (28.26-100.99, p=0.005) at random effect model. Conclusion: ML-FFR showed significant diagnostic values signifying that it can be used as a potential non-invasive alternative to iFFR for detecting lesion severity and guiding clinical decision-making in patients with suspected CAD, especially for those who are considered high-risk or ineligible for invasive angiography.

A Systematic Literature Review on Automated Software Vulnerability Detection Using Machine Learning

In recent years, numerous Machine Learning (ML) models, including Deep Learning (DL) and classic ML models, have been developed to detect software vulnerabilities. However, there is a notable lack of comprehensive and systematic surveys that summarize, classify, and analyze the applications of these ML models in software vulnerability detection. This absence may lead to critical research areas being overlooked or under-represented, resulting in a skewed understanding of the current state of the art in software vulnerability detection. To close this gap, we propose a comprehensive and systematic literature review that characterizes the different properties of ML-based software vulnerability detection systems using six major Research Questions (RQs). Using a custom web scraper, our systematic approach involves extracting a set of studies from four widely used online digital libraries: ACM Digital Library, IEEE Xplore, ScienceDirect, and Google Scholar. We manually analyzed the extracted studies to filter out irrelevant work unrelated to software vulnerability detection, followed by creating taxonomies and addressing RQs. Our analysis indicates a significant upward trend in applying ML techniques for software vulnerability detection over the past few years, with many studies published in recent years. Prominent conference venues include the International Conference on Software Engineering (ICSE), the International Symposium on Software Reliability Engineering (ISSRE), the Mining Software Repositories (MSR) conference, and the ACM International Conference on the Foundations of Software Engineering (FSE), whereas Information and Software Technology (IST), Computers & Security (C&S), and Journal of Systems and Software (JSS) are the leading journal venues. Our results reveal that 39.1% of the subject studies use hybrid sources, whereas 37.6% of the subject studies utilize benchmark data for software vulnerability detection. Code-based data are the most commonly used data type among subject studies, with source code being the predominant subtype. Graph-based and token-based input representations are the most popular techniques, accounting for 57.2% and 24.6% of the subject studies, respectively. Among the input embedding techniques, graph embedding and token vector embedding are the most frequently used techniques, accounting for 32.6% and 29.7% of the subject studies. Additionally, 88.4% of the subject studies use DL models, with recurrent neural networks and graph neural networks being the most popular subcategories, whereas only 7.2% use classic ML models. Among the vulnerability types covered by the subject studies, CWE-119, CWE-20, and CWE-190 are the most frequent ones. In terms of tools used for software vulnerability detection, Keras with TensorFlow backend and PyTorch libraries are the most frequently used model-building tools, accounting for 42 studies for each. In addition, Joern is the most popular tool used for code representation, accounting for 24 studies. Finally, we summarize the challenges and future directions in the context of software vulnerability detection, providing valuable insights for researchers and practitioners in the field.

AI solutions for overcoming delays in telesurgery and telementoring to enhance surgical practice and education

AbstractArtificial intelligence (AI) has emerged as a transformative tool in surgery, particularly in telesurgery and telementoring. However, its potential to enhance data transmission efficiency and reliability in these fields remains unclear. While previous reviews have explored the general applications of telesurgery and telementoring in specific surgical contexts, this review uniquely focuses on AI models designed to optimise data transmission and mitigate delays. We conducted a comprehensive literature search on PubMed and IEEE Xplore for studies published in English between 2010 and 2023, focusing on AI-driven, surgery-related, telemedicine, and delay-related research. This review includes methodologies from journals, conferences, and symposiums. Our analysis identified a total of twelve AI studies that focus on optimising network resources, enhancing edge computing, and developing delay-robust predictive applications. Specifically, three studies addressed wireless network resource optimisation, two proposed low-latency control and transfer learning algorithms for edge computing, and seven developed delay-robust applications, five of which focused on motion data, with the remaining two addressing visual and haptic data. These advancements lay the foundation for a truly holistic and context-aware telesurgical experience, significantly transforming remote surgical practice and education. By mapping the current role of AI in addressing delay-related challenges, this review highlights the pressing need for collaborative research to drive the evolution of telesurgery and telementoring in modern robotic surgery.

A Systematic Review of Accessibility Techniques for Online Platforms: Current Trends and Challenges

Accessibility in online platforms is a critical concern in our increasingly digital world, where information and services are predominantly accessed through the Internet. The purpose of this systematic review is to provide a comprehensive overview of the current state of the art in online accessibility technologies, and it is focused on key tools such as sign language recognition, speech-to-text, text-to-speech, and voice recognition. Despite advancements in digital inclusivity, numerous technical limitations persist, which limit the accessibility of online content for individuals with disabilities. Our findings indicate that while speech and voice technologies have achieved good accuracies and low word error rates, further research is needed to improve the accuracy and usability of sign language recognition systems, especially for continuous sign language recognition, as they have low accuracy. In this review, we analyzed research articles and publications from well-known databases, including Google Scholar, Elsevier, IEEE Xplore, and Springer. In order to ensure a high standard of quality, we applied the PRISMA 2020 and PEDro methodologies to quantitatively and qualitatively filter the thousands of articles provided by these databases, and we selected only studies that were related to our study. Key areas of investigation included the performance and accuracy of sign language interfaces, speech-to-text, text-to-speech, and speech recognition applications and the compatibility of these technologies with different platforms and devices. This review also explores the role of emerging technologies such as artificial intelligence (AI) and machine learning (ML) in enhancing accessibility and personalizing user experiences. Through a critical analysis of current solutions and a discussion of existing gaps, this paper offers insights into potential improvements and future directions for creating more accessible online environments. The findings might be valuable to researchers and developers dedicated to promoting digital inclusivity and equality.

Challenges of the Biopharmaceutical Industry in the Application of Prescriptive Maintenance in the Industry 4.0 Context: A Comprehensive Literature Review

The biopharmaceutical industry has specificities related to the optimization of its processes, the effectiveness of the maintenance of the productive park in the face of regulatory requirements. and current concepts of modern industry. Current research on the subject points to investments in the health area using the current tools and concepts of Industry 4.0 (I4.0) with the objective of a more assertive production, reduction of maintenance costs, reduction of operating risks, and minimization of equipment idle time. In this context, this study aims to characterize the current knowledge about the challenges of the biopharmaceutical industry in the application of prescriptive maintenance, which derives from predictive maintenance, in the context of I4.0. To achieve this, a systematic review of the literature was carried out in the scientific knowledge bases IEEE Xplore, Scopus, Web of Science, Science Direct, and Google Scholar, considering works such as Reviews, Article Research, and Conference Abstracts published between 2018 and 2023. The results obtained revealed that prescriptive maintenance offers opportunities for improvement in the production process, such as cost reduction and greater proximity to all actors in the areas of production, maintenance, quality, and management. The limitations presented in the literature include a reduced number of models, the lack of a clearer understanding of its construction, lack of applications directly linked to the biopharmaceutical industry, and lack of measurement of costs and implementation time of these models. There are significant advances in this area including the implementation of more elaborate algorithms used in artificial intelligence neural networks, the advancement of the use of decision support systems as well as the collection of data in a more structured and intelligent way. It is concluded that for the adoption of prescriptive maintenance in the pharmaceutical industry, issues such as the definition of data entry and analysis methods, interoperability between “shop floor” and corporate systems, as well as the integration of technologies existing in the world, must be considered for I4.0.

Enhancing IoT Security in Vehicles: A Comprehensive Review of AI-Driven Solutions for Cyber-Threat Detection

Background: The Internet of Things (IoT) has improved many aspects that have impacted the industry and the people’s daily lives. To begin with, the IoT allows communication to be made across a wide range of devices, from household appliances to industrial machinery. This connectivity allows for a better integration of the pervasive computing, making devices “smart” and capable of interacting with each other and with the corresponding users in a sublime way. However, the widespread adoption of IoT devices has introduced some security challenges, because these devices usually run in environments that have limited resources. As IoT technology becomes more integrated into critical infrastructure and daily life, the need for stronger security measures will increase. These devices are exposed to a variety of cyber-attacks. This literature review synthesizes the current research of artificial intelligence (AI) technologies to improve IoT security. This review addresses key research questions, including: (1) What are the primary challenges and threats that IoT devices face?; (2) How can AI be used to improve IoT security?; (3) What AI techniques are currently being used for this purpose?; and (4) How does applying AI to IoT security differ from traditional methods? Methods: We included a total of 33 peer-reviewed studies published between 2020 and 2024, specifically in journal and conference papers written in English. Studies irrelevant to the use of AI for IoT security, duplicate studies, and articles without full-text access were excluded. The literature search was conducted using scientific databases, including MDPI, ScienceDirect, IEEE Xplore, and SpringerLink. Results were synthesized through a narrative synthesis approach, with the help of the Parsifal tool to organize and visualize key themes and trends. Results: We focus on the use of machine learning, deep learning, and federated learning, which are used for anomaly detection to identify and mitigate the security threats inherent to these devices. AI-driven technologies offer promising solutions for attack detection and predictive analysis, reducing the need for human intervention more significantly. This review acknowledges limitations such as the rapidly evolving nature of IoT technologies, the early-stage development or proprietary nature of many AI techniques, the variable performance of AI models in real-world applications, and potential biases in the search and selection of articles. The risk of bias in this systematic review is moderate. While the study selection and data collection processes are robust, the reliance on narrative synthesis and the limited exploration of potential biases in the selection process introduce some risk. Transparency in funding and conflict of interest reporting reduces bias in those areas. Discussion: The effectiveness of these AI-based approaches can vary depending on the performance of the model and the computational efficiency. In this article, we provide a comprehensive overview of existing AI models applied to IoT security, including machine learning (ML), deep learning (DL), and hybrid approaches. We also examine their role in enhancing the detection accuracy. Despite all the advances, challenges still remain in terms of data privacy and the scalability of AI solutions in IoT security. Conclusion: This review provides a comprehensive overview of ML applications to enhance IoT security. We also discuss and outline future directions, emphasizing the need for collaboration between interested parties and ongoing innovation to address the evolving threat landscape in IoT security.

Sensing In Exergames for Efficacy and Motion Quality: Scoping Review of Recent Publications.

Many studies have shown a direct relationship between physical activity and health. It has also been shown that the average fitness level in Western societies is lower than recommended by the World Health Organization. One tool that can be used to increase physical activity for individual people is exergaming, that is, serious games that motivate players to do physical exercises. This scoping review of recent studies regarding exergame efficacy aims to evaluate which sensing modalities are used to assess exergame efficacy as well as motion quality. We also analyze how the collected motion sensing data is being leveraged with respect to exergame efficacy and motion quality assessment. We conducted 2 extensive and systematic searches of the ACM Digital Library and the PubMed database, as well as a single search of the IEEE Xplore database, all according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. Overall, 343 studies were assessed for eligibility by the following criteria: The study should be peer-reviewed; the year of publication should be between 2015 and 2023; the study should be available in English or German; the study evaluates the efficacy of at least 1 exergame; sensor data is recorded during the study and is used for evaluation; and the study is sufficiently described to extract information on the exergames, sensors, metrics, and results. We found 67 eligible studies, which we analyzed with regard to sensor usage for both efficacy evaluation and motion analysis. Overall, heart rate (HR) was the most commonly used vital sign to evaluate efficacy (n=52), while the Microsoft Kinect was the most commonly used exergame sensor (n=26). The results of the analysis show that the sensors used in the exergames and the sensors used in the evaluation are, in most cases, mutually exclusive, with motion quality rarely being considered as a metric. The lack of motion quality assessment is identified as a problem both for the studies and the exergames themselves since incorrectly executed motions can reduce an exergame's effectiveness and increase the risk of injury. Here we propose how to use the same sensors both as input for the exergame and to assess motion quality by presenting recent developments in motion recognition and sensing.

Accuracy Standards of Wearable Technologies for Assessment of Soccer Kicking: Protocol for a Systematic Literature Review.

Wearable technology is widely applied in performance monitoring, an integral part of sports and exercise sciences. The kick movement in soccer exemplifies a sports technique that could benefit from appropriate biomechanics assessment methodologies. However, the accuracy of wearables in quantifying soccer kick mechanics, particularly under field conditions, remains unclear. This study aims to present a protocol for a systematic review to discuss the measurement properties (validity, reliability, and/or accuracy aspects) of wearable technology systems explicitly used to measure ball-kicking features in soccer. This review protocol was preregistered in the Open Science Framework. A total of 2 authors will perform searches in major electronic databases using specific keyword combinations in PubMed, Physical Therapy and Sports Medicine, Web of Science, ProQuest, IEEE Xplore, EBSCOHost, and Scopus. Following a specific population, intervention, comparison, outcome framework (population: soccer players and/or collected human data in a football-related environment; intervention: at least 1 wearable used; comparator: criterion measures, repeated testing sessions and/or actual values; outcome: ball kicking data), studies will be screened based on predetermined inclusion and exclusion criteria. The methodological quality of the included studies will be assessed using the "consensus-based standards for the selection of health measurement instruments" checklist (in studies concerning validity or reliability) or the "quality assessment of diagnostic accuracy studies" tool (in studies concerning accuracy). Data extraction will be conducted to determine the level of evidence according to the "best evidence synthesis method," and an evidence gap map will be constructed. The Cohen κ coefficient will be used to estimate the interevaluator agreement. This ongoing systematic review has completed database searches and is currently in the screening phase. Depending on the number and consistency of studies, results may be presented by meta-analysis or qualitative synthesis, with subgroup analyses considering factors such as gender, age, and playing level. The final results are expected by July 2024, with manuscript submission anticipated by November 2024. Our study will provide a comprehensive summary of the highest level of evidence available on the use of wearables for the assessment of soccer kick mechanics, providing practical guidance for athletes and sports sciences professionals regarding the validity and reliability aspects of using wearable technology to measure ball-kicking features in soccer. OSF registries https://osf.io/zm3j6. DERR1-10.2196/57433.

The Relation Between Passively Collected GPS Mobility Metrics and Depressive Symptoms: Systematic Review and Meta-Analysis.

The objective, unobtrusively collected GPS features (eg, homestay and distance) from everyday devices like smartphones may offer a promising augmentation to current assessment tools for depression. However, to date, there is no systematic and meta-analytical evidence on the associations between GPS features and depression. This study aimed to investigate the between-person and within-person correlations between GPS mobility and activity features and depressive symptoms, and to critically review the quality and potential publication bias in the field. We searched MEDLINE, PsycINFO, Embase, CENTRAL, ACM, IEEE Xplore, PubMed, and Web of Science to identify eligible articles focusing on the correlations between GPS features and depression from December 6, 2022, to March 24, 2023. Inclusion and exclusion criteria were applied in a 2-stage inclusion process conducted by 2 independent reviewers (YT and JK). To be eligible, studies needed to report correlations between wearable-based GPS variables (eg, total distance) and depression symptoms measured with a validated questionnaire. Studies with underage persons and other mental health disorders were excluded. Between- and within-person correlations were analyzed using random effects models. Study quality was determined by comparing studies against the STROBE (Strengthening the Reporting of Observational studies in Epidemiology) guidelines. Publication bias was investigated using Egger test and funnel plots. A total of k=19 studies involving N=2930 participants were included in the analysis. The mean age was 38.42 (SD 18.96) years with 59.64% (SD 22.99%) of participants being female. Significant between-person correlations between GPS features and depression were identified: distance (r=-0.25, 95% CI -0.29 to -0.21), normalized entropy (r-0.17, 95% CI -0.29 to -0.04), location variance (r-0.17, 95% CI -0.26 to -0.04), entropy (r=-0.13, 95% CI -0.23 to -0.04), number of clusters (r=-0.11, 95% CI -0.18 to -0.03), and homestay (r=0.10, 95% CI 0.00 to 0.19). Studies reporting within-correlations (k=3) were too heterogeneous to conduct meta-analysis. A deficiency in study quality and research standards was identified: all studies followed exploratory observational designs, but no study referenced or fully adhered to the international guidelines for reporting observational studies (STROBE). A total of 79% (k=15) of the studies were underpowered to detect a small correlation (r=.20). Results showed evidence for potential publication bias. Our results provide meta-analytical evidence for between-person correlations of GPS mobility and activity features and depression. Hence, depression diagnostics may benefit from adding GPS mobility and activity features as an integral part of future assessment and expert tools. However, confirmatory studies for between-person correlations and further research on within-person correlations are needed. In addition, the methodological quality of the evidence needs to improve. OSF Registeries cwder; https://osf.io/cwder.

E-leadership Practices and Implication for Higher Educations: A Systematic Review Protocol

E-leadership practices in developed countries are employed for leadership in business and industry which is an applicable system that is advantageous for leaders and employers to connect simply through technology. However, e-leadership practices in higher education, specifically in developing countries, need further investigation. To gain insight into this gap in e-leadership practices in higher education, this systematic review will search and analyze e-leadership practices from developed to developing countries in different disciplines with different methodologies which have implications for higher education. For this systematic review, the databases GOOGLE SCHOLAR, PUBMED and IEEE XPLORE will be utilized. Articles published in the English language that focus on e-leadership practices in different disciplines will be eligible. The evidence will be searched and data will be extracted on the similarities and differences between e-leadership practices in previous research. Indicating the searched evidence in methodology, geography, and discipline will determine how many e-leadership practices exist. I will ensure that all articles are checked and address e-leadership practices in different disciplines and contexts to provide vital ideas and implications for higher educations.

A Systematic Literature Review on Knowledge Management in Healthcare: Best Practices and Future Directions

The role of knowledge management (KM) in improving the standard and effectiveness of healthcare is gaining more attention. However, because of the complexity and the sensitive nature of medical information, the use of knowledge management approaches in this industry faces unique challenges. In order to identify best practices, summarize current research on knowledge management (KM) in healthcare, and suggest future research paths, a comprehensive literature review was conducted. A detailed search was carried out across many important databases, including PubMed, Scopus, IEEE Xplore and Web of Science, in order to gather the relevant studies for the review. The review focused on studies published between 2010 and 2024 that specifically addressed KM practices within healthcare settings and adhered to the PRISMA guidelines for systematic reviews. The review found 50 papers that satisfied the requirements for inclusion. Significant findings illustrate how important organizational culture, leadership backing, and technology infrastructure are, to the effective use of knowledge management. Some of the most significant techniques in Knowledge Management that are emphasized are creating communities of practice, fusing KM systems with medical guidelines, and employing data analytics for knowledge creation. On the other side, challenges include hesitation to change and interoperability issues

DEVELOPMENT OF INNOVATIVE MEDICAL DEVICES IN NEONATOLOGY: LITERATURE REVIEW

The aim of this study is to review the development of medical devices in neonatology, exploring technological advances, identifying innovations and challenges, and proposing recommendations for future research. The methodology involved a narrative review of the literature, searching databases such as PubMed, EBSCO, Cochrane, and IEEE Xplore, focusing on studies from the last ten years on medical devices for neonatology. The results highlight that technologies such as artificial intelligence and machine learning have improved the monitoring and prognosis of neonatal conditions, allowing for more precise and personalized interventions. 3D printing emerges as a promising tool for creating personalized medical devices, meeting the specific needs of each patient. Wearable devices for continuous monitoring of vital signs and advanced enteral and parenteral feeding devices have demonstrated a positive impact on neonatal health. Mechanical ventilators with gentler and smarter modes have reduced serious complications, and catheters with antimicrobial coatings have helped to reduce the incidence of neonatal sepsis. The conclusions indicate that technological innovations are fundamental to improving neonatal care. The integration of artificial intelligence, 3D printing and continuous monitoring has the potential to transform neonatology, improving clinical outcomes and the quality of life of newborns. However, challenges such as the variability of neonatal pathologies, cost and accessibility of devices and the need for adequate training of health professionals need to be addressed. Investment in continuous research, clinical validation and interdisciplinary training is recommended to ensure the effective adoption of these innovative technologies in daily clinical practices.

Digital Mental Health Interventions for Adolescents in Low- and Middle-Income Countries: Scoping Review.

Digital mental health interventions (DMHIs) are increasingly recognized as potential solutions for adolescent mental health, particularly in low- and middle-income countries (LMICs). The United Nations' Sustainable Development Goals and universal health coverage are instrumental tools for achieving mental health for all. Within this context, understanding the design, evaluation, as well as the barriers and facilitators impacting adolescent engagement with mental health care through DMHIs is essential. This scoping review aims to provide insights into the current landscape of DMHIs for adolescents in LMICs. The Joanna Briggs Institute scoping review methodology was used, following the recommendations of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). Our search strategy incorporated 3 key concepts: population "adolescents," concept "digital mental health interventions," and context "LMICs." We adapted this strategy for various databases, including ACM Digital Library, APA PsycINFO, Cochrane Library, Google Scholar (including gray literature), IEEE Xplore, ProQuest, PubMed (NLM), ScienceDirect, Scopus, and Web of Science. The articles were screened against a specific eligibility criterion from January 2019 to March 2024. We analyzed 20 papers focusing on DMHIs for various mental health conditions among adolescents, such as depression, well-being, anxiety, stigma, self-harm, and suicide ideation. These interventions were delivered in diverse formats, including group delivery and self-guided interventions, with support from mental health professionals or involving lay professionals. The study designs and evaluation encompassed a range of methodologies, including randomized controlled trials, mixed methods studies, and feasibility studies. While there have been notable advancements in DMHIs for adolescents in LMICs, the research base remains limited. Significant knowledge gaps persist regarding the long-term clinical benefits, the maturity and readiness of LMIC digital infrastructure, cultural appropriateness, and cost-effectiveness across the heterogeneous LMIC settings. Addressing these gaps necessitates large-scale, co-designed, and culturally sensitive DMHI trials. Future work should address this.

Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping Review.

The rapid advancement of digital technologies, particularly in big data analytics (BDA), artificial intelligence (AI), machine learning (ML), and deep learning (DL), is reshaping the global health care system, including in Bangladesh. The increased adoption of these technologies in health care delivery within Bangladesh has sparked their integration into health care and public health research, resulting in a noticeable surge in related studies. However, a critical gap exists, as there is a lack of comprehensive evidence regarding the research landscape; regulatory challenges; use cases; and the application and adoption of BDA, AI, ML, and DL in the health care system of Bangladesh. This gap impedes the attainment of optimal results. As Bangladesh is a leading implementer of digital technologies, bridging this gap is urgent for the effective use of these advancing technologies. This scoping review aims to collate (1) the existing research in Bangladesh's health care system, using the aforementioned technologies and synthesizing their findings, and (2) the limitations faced by researchers in integrating the aforementioned technologies into health care research. MEDLINE (via PubMed), IEEE Xplore, Scopus, and Embase databases were searched to identify published research articles between January 1, 2000, and September 10, 2023, meeting the following inclusion criteria: (1) any study using any of the BDA, AI, ML, and DL technologies and health care and public health datasets for predicting health issues and forecasting any kind of outbreak; (2) studies primarily focusing on health care and public health issues in Bangladesh; and (3) original research articles published in peer-reviewed journals and conference proceedings written in English. With the initial search, we identified 1653 studies. Following the inclusion and exclusion criteria and full-text review, 4.66% (77/1653) of the articles were finally included in this review. There was a substantial increase in studies over the last 5 years (2017-2023). Among the 77 studies, the majority (n=65, 84%) used ML models. A smaller proportion of studies incorporated AI (4/77, 5%), DL (7/77, 9%), and BDA (1/77, 1%) technologies. Among the reviewed articles, 52% (40/77) relied on primary data, while the remaining 48% (37/77) used secondary data. The primary research areas of focus were infectious diseases (15/77, 19%), noncommunicable diseases (23/77, 30%), child health (11/77, 14%), and mental health (9/77, 12%). This scoping review highlights remarkable progress in leveraging BDA, AI, ML, and DL within Bangladesh's health care system. The observed surge in studies over the last 5 years underscores the increasing significance of AI and related technologies in health care research. Notably, most (65/77, 84%) studies focused on ML models, unveiling opportunities for advancements in predictive modeling. This review encapsulates the current state of technological integration and propels us into a promising era for the future of digital Bangladesh.

Current application of artificial intelligence in laparoscopic cholecystectomy

Recent advances in artificial intelligence (AI) have sparked a surge in the application of computer vision (CV) in surgical video analysis. Surgical complications often occur due to lapses in judgment and decision-making. In laparoscopic cholecystectomy, achievement of the critical view of safety is commonly advocated to prevent bile duct injuries. However, bile duct injuries rates remain stable, probably due to inconsistent application or a poor understanding of critical view of safety. Advances in AI have made it possible to train algorithms that identify anatomy and interpret the surgical field. AI-based CV techniques may leverage surgical video data to develop real-time automated decision support tools and surgeon training systems. The effectiveness of CV application in surgical procedures is still under early evaluation. The review considers the commonly used deep learning algorithms in CV and describes their usage in detail in four application scenes, including phase recognition, anatomy detection, instrument detection and action recognition in laparoscopic cholecystectomy. The MedLine, Scopus, and IEEE Xplore databases were searched for publications up to 2024. The keywords used in the search were “laparoscopic cholecystectomy”, “artificial intelligence”. The currently described applications of CV in laparoscopic cholecystectomy are limited. Most current research focus on the identification of workflow and anatomical structure, while the identification of instruments and surgical actions is still awaiting further breakthroughs. Future research on the use of CV in laparoscopic cholecystectomy should focus on application in more scenarios, such as surgeon skill assessment and the development of more efficient models.

Predicting Biochemical Recurrence of Prostate Cancer Post-Prostatectomy Using Artificial Intelligence: A Systematic Review

Background/Objectives: Biochemical recurrence (BCR) after radical prostatectomy (RP) is a significant predictor of distal metastases and mortality in prostate cancer (PCa) patients. This systematic review aims to evaluate the accuracy of artificial intelligence (AI) in predicting BCR post-RP. Methods: Adhering to PRISMA guidelines, a comprehensive literature search was conducted across Medline, Embase, Web of Science, and IEEE Xplore. Studies were included if they utilised AI to predict BCR in patients post-RP. Studies involving patients who underwent radiotherapy or salvage RP were excluded. This systematic review was registered on PROSPERO (International prospective register of systematic reviews) under the ID CRD42023482392. Results: After screening 9764 articles, 24 met the inclusion criteria. The included studies involved 27,216 patients, of whom 7267 developed BCR. AI algorithms developed using radiological parameters demonstrated higher predictive accuracy (median AUROC of 0.90) compared to algorithms based solely on pathological variables (median AUROC of 0.74) or clinicopathological variables (median AUROC of 0.81). According to the Prediction Model Risk of Bias Assessment Tool (PROBAST), the overall risk of bias was unclear in three studies due to ambiguous inclusion criteria and the exclusion of many patients because of missing follow-up data. In seven studies, the developed AI outperformed or was at least equivocal to traditional methods of BCR prediction. Conclusions: AI shows promise in predicting BCR post-RP, particularly when radiological data were used in its development. However, the significant variability in AI performance and study methodologies highlights the need for larger, standardised prospective studies with external validation prior to clinical application.

Smart Home Technologies for Enhancing Independence of Living and Reducing Care Dependence in Older Adults: A Systematic Review.

To systematically review the potential of smart home technology to enhance the independence of older adults and reduce their dependence on care. Additionally, it sought to examine the positive impacts of such technology on their golden years. A systematic review based on Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). The search was conducted on 8 April 2024. Peer-reviewed studies in PubMed, Embase, Web of Science, IEEE Xplore, Scopus, The Cochrane Library, CINAHL, CNKI, WANFANG DATA and VIP from 1 January 2000 to 8 April 2024 were searched. The methodological quality assessment used the Mixed Methods Appraisal Tool (MMAT). Positive findings relevant to this study were extracted from the literature and analysed using thematic synthesis. After meticulously examining 3404 studies, we identified 21 relevant sources for in-depth analysis, including qualitative studies (n = 10), experimental studies (n = 9) and mixed method studies (n = 2). These sources were grouped into five core themes based on the pivotal role of smart home technologies in enabling ageing in place: daily monitoring, assisted living activities, life reminders, functional improvement and emotional companionship. The study found that smart home technology offers numerous benefits to the lives of older adults, including increased independence, psychological support, improved cognitive functioning, enhanced self-management, increased mobility, support for caregivers, promoted social engagement and enhanced quality of life. Smart home technology can enhance the independence of older adults' lives, reduce their dependence on care, alleviate the burden on caregivers and promote home-based elderly care. This systematic review contributes to understanding the capability of smart home technology to promote elderly care at home and help better utilise smart home technology to benefit older adults. Older adults and their caregivers should be encouraged to adopt this technology to improve older adults' quality of life. No patient or public contribution.