Daksh Dave, Adnan Akhunzada, Nikola Ivković
et al.
The integration of artificial intelligence into healthcare, particularly in mammography, holds immense potential for improving breast cancer diagnosis. Artificial intelligence (AI), with its ability to process vast amounts of data and detect intricate patterns, offers a solution to the limitations of traditional mammography, including missed diagnoses and false positives. This review focuses on the diagnostic accuracy of AI-assisted mammography, synthesizing findings from studies across different clinical settings and algorithms. The motivation for this research lies in addressing the need for enhanced diagnostic tools in breast cancer screening, where early detection can significantly impact patient outcomes. Although AI models have shown promising improvements in sensitivity and specificity, challenges such as algorithmic bias, interpretability, and the generalizability of models across diverse populations remain. The review concludes that while AI holds transformative potential in breast cancer screening, collaborative efforts between radiologists, AI developers, and policymakers are crucial for ensuring ethical, reliable, and inclusive integration into clinical practice.
Background The growing scale and complexity of Internet of Things (IoT) environments have intensified the need for intelligent and adaptive cybersecurity mechanisms. Artificial intelligence (AI)-based intrusion detection systems (IDS) have emerged as a promising solution for identifying and mitigating threats in real time. Methodology This review systematically evaluates the effectiveness of AI-based IDS in IoT networks, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. A comprehensive search of the Scopus and Web of Science databases was conducted, yielding 203 studies, of which 51 met the inclusion criteria. Eligible studies, published between 2016 and 2025, were analyzed for geographic distribution, AI techniques used, methodological quality, and reported outcomes. Meta-regression and contour-enhanced funnel plots were employed to assess effect size trends and publication bias. Results Most studies originated from India, Saudi Arabia, and China, with research output peaking in 2024. Meta-regression analysis revealed a positive correlation between publication year and reported effect size, indicating progressive advancements in AI methodologies. Machine learning (ML) and deep learning (DL) were the most widely used techniques, with a growing trend toward hybrid and ensemble models that enhance threat detection accuracy. Recent studies also showed increased interest in explainable artificial intelligence (XAI), reflecting the demand for transparency and interpretability in model outputs. Funnel plot asymmetry suggested a bias toward publishing positive findings. Conclusions AI-based IDSs have demonstrated substantial potential in strengthening IoT security, particularly through ML, DL, and hybrid approaches. However, inconsistencies in evaluation metrics, reporting standards, and methodological design remain significant challenges. The findings highlight the need for standardized benchmarks and robust frameworks to guide future research and ensure reliable deployment of AI-driven IDS in diverse IoT contexts.
Abstract Aim Meningioma surgery is associated with increased intraoperative bleeding and transfusion requirement. Tranexamic Acid (TXA) has been used in medical and surgical practice to reduce haemorrhage. This review aimed to evaluate the effect of TXA use on bleeding and transfusion requirement, and functional outcomes in meningioma surgery. Method A systematic review and meta-analysis was conducted in accordance with the PRISMA statement and registered in PROSPERO (CRD42021292157). Six databases were searched up to November 2021. Random effects meta-analysis was performed to delineate blood loss, transfusion requirements, postoperative complications, operation time, and hospital stay. Results Four studies (181 patients) were included (three randomised control trials, one clinical trial). TXA use significantly reduced Intraoperative blood loss (mean difference 315.69mls [95% CI -532.94 to -98.54]) and transfusion requirement (OR 0.52 [95% CI 0.27–0.98]). Factors not affected by TXA use were operation time (mean difference 0.09 hours [95% CI -0.11 to 0.29]), Post-operative seizures (OR 0.87 [95% CI 0.31–2.49]), hospital stay (mean difference -2.4 days [95% CI -3.18 to -1.62]), and disability after surgery (OR 0.47 [95% CI 0.14–1.61]). Risk of bias was low (n=3) or unclear (n=1) in all included studies. Heterogeneity was high in length of operation (I2= 90%), and hospital stay outcomes (I2= 92%), and low in all others. Conclusions TXA use reduces blood loss and transfusion requirement in meningioma surgery, but not post-operative complications, or disability after surgery. Larger trials are required to investigate the impact of TXA on patient-focussed post-operative outcomes.
El artículo presenta la llegada del nuevo milenio, un número cada vez mayor de empresarios se unieron a la aplicación del diseño sostenible que comenzó a replantearse en las empresas y el rol que juegan con el desarrollo del medio ambiente, el planeta y en la sociedad. Podemos decir que el diseño sostenible busca generar soluciones a través de servicios y estilos de vida, pero no exclusivamente a través de objetos. Con el fin de introducir una definición elaborada de diseño sostenible es necesario mencionar los sistemas sostenibles, que básicamente, se refieren a cualquier tipo de red o servicio social que puede existir y replicarse. Además de sistemas sostenibles hay otros principios dentro del diseño sostenible. Por último, cualquier tipo de resultado obtenido para satisfacer la necesidad debe ser sostenible a largo plazo entendiéndose como un proceso que permita una comunidad lograr un resultado a través de estrategias de diseño.
The integration of Virtual Reality (VR)and Conversational AI in education has modernized how students engage with educational and career guidance. Despite the growing integration of VR and Conversational AI in education, empirical studies on their combined impact on students’ intentions to pursue computer science remain limited. This study proposes the Conversational AI-empowered Virtual Reality System (CAI-VR system)as an educational guidance tool designed to enhance high school students' intentions to pursue a Bachelor of Science in Computer Science at RMUTT. We employed a developmental research method to develop and evaluate the system. The findings indicate that 1) the CAI-VR system demonstrates the convergence of VR, conversational AI, and multimedia elements in delivering an immersive 360-degree virtual experience. Users can access the system in two modes, i.e., Immersive VR Mode for full interactivity via VR devices and Web-Based Display Mode for browser access; 2) Usability was assessed with 30 high school students, while five experts evaluated system quality. Both assessments confirmed the system’s effectiveness; 3) the system significantly increased students’ intention scores, reinforcing its role in guiding educational decision-making; 4) further evaluation with 400 students demonstrated high awareness; however, behavioral commitment—especially in planning, preparation, and application—was lower than cognitive or affective aspects. Overall, students reported a high level of satisfaction with the system (M = 3.99, SD = 0.88). These findings suggest that the CAI-VR system can effectively support digital-native students’ educational and career decision-making. Further development should deepen learner engagement via gamified, collaborative, and personalized experiences.