European Journal of Science, Innovation and Technology

Transformation of the Product Manager Role under Artificial Intelligence Integration

2026-06-26T21:54:52+03:00

The article examines how artificial intelligence is restructuring product management in digital organizations. The study addresses the growing tension between accelerating automation in delivery routines and the rising demand for strategic product judgment. Its purpose is to determine which layers of product work are being compressed by AI, which functions are expanding, and how the center of gravity of the product manager role is shifting. The analytical design combines source analysis, comparative interpretation, conceptual synthesis, and structural generalization. The materials include recent studies on digital product management, AI adoption in software organizations, human-centered design of AI-enabled work, responsible AI governance, and industry-wide evidence on enterprise implementation. The results show that AI reduces the relative weight of routine coordination, drafting, and first-pass analysis, while increasing the value of system tuning, data instrumentation, human oversight, cross-functional alignment, and organizational governance. Practical applicability lies in redefining product-management competencies, decision architectures, and operating models for AI-integrated product environments.

AI-Driven Patient Engagement Ecosystems: Advancing Predictive Analytics and Personalised Care Through Multimodal Dental Data

2026-07-13T22:52:00+03:00

Fragmented dental information flows hinder the early detection of oral diseases and limit the personalisation of care, despite rapid progress in artificial intelligence and telehealth. The article outlines an AI-driven patient engagement ecosystem, grounded in multimodal dental data, that integrates imaging, electronic health records, demographic information, and patient-generated data from mobile and teledentistry channels. The aim is to align multimodal predictive analytics with continuous, personalised engagement at both individual and population levels. The study relies on an analytical synthesis of recent work on dental AI, public health–oriented predictive models, teledentistry platforms, and multimodal machine learning architectures. Dental AI Engagement Ecosystem (DAIE) connects curated dental datasets and fusion models with patient-facing services, including remote triage, conversational agents, risk-based recalls, and tailored behavioural support. Special attention is paid to vulnerable populations, the governance of data flows, and the explainability of predictions across clinical and home settings. The framework provides methodological and practical guidance for clinicians, health system managers, and digital health developers designing AI-ready dental care pathways.

Graph Neural Networks for Fraudulent Transaction Pattern Detection in Digital Lending Systems

2026-07-13T22:54:02+03:00

Digital lending systems handle borrower applications, repayment events, device traces, and payment flows in environments where fraud arises from relationships among actors. A single application record rarely exposes coordinated fraud, while shared devices, repeated accounts, recycled contacts, and post-disbursement transfers often reveal hidden links. This article examines the use of Graph Neural Networks for detecting fraudulent transaction patterns in digital lending, with particular attention to underbanked and thin-file borrowers. The study aims to build an analytical model that connects graph construction, fraud pattern recognition, and decision governance. The material base covers recent studies on financial fraud detection, consumer loan fraud, graph anomaly detection, imbalanced learning and online credit loan risk. Comparative source analysis, conceptual synthesis, and typologization guide the research design. The article identifies graph representation principles, GNN mechanisms for coordinated fraud detection, and implementation rules for credit decision workflows. The proposed model supports auditable fraud controls without replacing creditworthiness assessment.