European Journal of Science, Innovation and Technology

Contemporary Challenges in IT: AI in Healthcare and Education

Bongs Lainjo — 2025-08-20

The article examines the modern issues and possibilities of Artificial Intelligence (AI), both in healthcare and education, as two of the most pressing spheres of society that require AI implementation. As countries stay on track to achieve the United Nations Sustainable Development Goals, specifically SDG 3 (Good Health and Well-being) and SDG 4 (Quality Education), AI has become a disruptive tool that could change the way service delivery is done, how people access services, and the outcome. However, the paper stresses that AI could worsen the current inequalities unless close attention is paid to these issues through careful ethical governance, inclusive design, and investment in digital infrastructure.

AI applications in healthcare include various types of diagnostic imaging, telemedicine, clinical decision support systems, and new models of care delivered through technologies such as NLP and predictive analytics. The case studies of the U.S. and Rwanda also represent the opportunities and hazards of AI, such as diagnostic level accuracy, data fragmentation problem, algorithm bias, and minimal clinical validation.

In education, AI is used in the form of intelligent tutoring systems, adaptive assessments, and automation of administration and learning. Such efforts as adaptive learning provided by Arizona State University and the Rori AI tutor project in Ghana show opportunities related to equity and scalability. However, there is still some worry about privacy, algorithm discrimination, and ineffective teacher training.

Through a comparative analysis of the two sectors, the article provides an insightful look into their similarities, including infrastructure requirements and data bias, as well as the differences between user adoption, regulation, and risk tolerance. It proclaims cross-sectoral policy frameworks, capacity-building programs, and an ethics approach to AI design to guarantee sustainable and fair AI adoption.

Machine Learning in Healthcare

Ahmed Karam — 2025-08-22

Machine learning (ML) has emerged as a center of gravity in the healthcare industry, providing an unequaled capacity to perform prodigious and intricate processes to frame better decisions, diagnoses, and therapies. ML permits early diagnosis of illnesses, predictive analytics of patient outcomes, and individualized treatment planning via utilizing the patterns of the observed data with the implementation of algorithms that can be educated. The paradigm shift is fueled by the speedy expansion of electronic health records (EHRs), medical imaging repositories, wearable device outputs, and genomic datasets. The Healthcare ML applications range widely in scope, with some of their uses being computer vision in treating radiology and pathology, natural language processing to analyze raw clinical notes, population health management, predictive analytics, and others. Next, various operational efficiencies are attained in ML-based scheduling, resource allocation, and fraud detection systems. Nonetheless, implementing ML technology into clinical practice is not problem-free, and the following factors should still be considered: low data quality, bias in the ML model, privacy, and compliance with regulations. Countermeasures against these obstacles in the form of federated learning, explainable AI, and resilient governance systems are on the rise, allowing for more secure and fairer implementation. The paper will summarize principles, essential applications, technical and ethical aspects, and practical case scenarios to comprehensively see ML in the healthcare industry. It also provides an overview of how the intersection of technical innovation and clinical relevance has the potential to transform patient care, as well as amplify the effectiveness of clinical care and have an impact on improving patient health at every level. Finally, achieving this potential ought to necessitate interdisciplinary approaches, critical assessment, and ethical innovation so that ML-based healthcare systems can be precise, responsible, and reflective of patient health.

The TriAxis Culture Index (TAC Index): A New Paradigm for Organizational Culture

Akshay Dipali — 2025-08-25

For years, companies have measured workplace culture using tools like annual surveys and eNPS scores. These methods were simple, but often too slow, too shallow, and too disconnected from business outcomes to drive real change. This paper introduces the TriAxis Culture Index (TAC Index) as a new approach, one that treats culture not just as an HR concern, but as a strategic system that can be measured, managed, and improved in real time.

The TriAxis Culture Index (TAC Index) is built around three core cultural indicators: Feedback Loop Velocity (FLV), Sentiment Resilience Score (SRS), and the Employee-Generated Revenue Index (EGRI). Each metric captures a distinct dimension of how people experience work from how quickly feedback is acted upon, to how effectively teams recover from stress, to how much employee-driven innovation and effort contribute to revenue. By combining these indicators with AI-powered analysis, the TAC Index delivers an integrated scorecard that connects culture directly to performance, risk, and engagement.

This paper explores how the TAC Index addresses the limitations of traditional surveys, and how it offers both HR and executive leaders a clearer way to understand and act on culture. Drawing from industry case examples and academic insights, we show how organizations using the TAC Index can detect early signs of disengagement, improve decision-making, and align culture with long-term business goals. In a world where workforce sentiment shifts quickly, this framework helps organizations stay adaptive, resilient, and grounded in data.

Construction Safety Management System Implementation: Current Practices and Gaps in the Indonesian Construction Industry

Kaharuddin Kaharuddin — 2025-08-25

The implementation of the Construction Safety Management System (CSMS) is a critical requirement for ensuring worker safety, public protection, and project success in the Indonesian construction industry. Despite the existence of regulatory frameworks such as Minister of Public Works and Housing Regulation (Permen PUPR) No. 10 of 2021, the level of CSMS implementation varies significantly across projects. This study evaluates the current practice of CSMS in a road and bridge preservation project: the BTS Kota Palopo – BTS Kabupaten Luwu project in South Sulawesi, executed by PT. Millenium Persada. A mixed-method approach was employed, combining field observations, document reviews, and structured interviews with project personnel, guided by the audit criteria in Permen PUPR No. 10 of 2021. The assessment covered five core elements of CSMS: Leadership and Worker Participation, Hazard Identification and Risk Assessment, Operational Safety Management, Internal Audit, and Management Review. The results indicate an overall implementation level of 80.93%, categorized as "Good" according to Government Regulation No. 50 of 2012. However, several gaps were identified, including inconsistent communication, inadequate supervision, insufficient safety training, and non-compliance in equipment operation procedures. Using Analytic Hierarchy Process (AHP) analysis via Expert Choice software, key improvement strategies were prioritized: strengthening internal supervision, enhancing safety training programs, improving safety signage, and adopting digital monitoring tools. This study provides empirical evidence of CSMS performance in infrastructure preservation projects and offers practical recommendations for contractors and regulatory bodies to improve safety outcomes and move toward zero accident goals.