Over recent years, driven by intertwined economic, social, environmental, and technological factors, urbanization has accelerated at an unprecedented pace, posing complex challenges to metropolitan transport systems. This has intensified the demand for innovative mobility solutions, notably Mobility as a Service (MaaS), which promotes a paradigm shift from private vehicle ownership to mobility consumed as a service. With rapid advances in digital technologies, MaaS has gained substantial momentum, attracting significant scholarly attention for its potential to enable intelligent and sustainable transportation systems. This study aims to provide a comprehensive conceptual foundation of MaaS and its components, and to systematically examine how artificial intelligence (AI), machine learning (ML), and big data techniques are applied in this domain. Following PRISMA guidelines, a bibliometric and systematic review was conducted on peer-reviewed articles published between 2020 and 2024 and indexed in the Scopus and Web of Science databases. The analysis classifies AI applications across four MaaS integration levels: basic, intermediate, advanced, and full integration. The results show that machine learning and basic optimization dominate at the basic level; blockchain and big data are most prominent at the advanced and full levels; and deep learning is applied across all levels, with a particularly strong presence at the advanced stage for real-time, personalized mobility solutions. The findings also indicate that while most implementations focus on developed countries, there is substantial potential for adaptation in emerging markets. The paper concludes by discussing key challenges in regulatory compliance, inclusivity, and the protection of sensitive user data, and outlines future research avenues for building socially equitable, intelligent, and sustainable MaaS ecosystems.

Timely and precise identification of breast cancer is crucial for enhancing clinical outcomes; however, current AI systems frequently exhibit deficiencies in transparency, trustworthiness, and the capacity to assimilate varied data modalities. We introduce a reliable, multi-agent, multimodal AI system for individualised early breast cancer diagnosis, created on the CBIS-DDSM dataset. The system consists of four specialised agents that cooperatively analyse diverse data. An Imaging Agent employs convolutional and transformer-based models to analyse mammograms for lesion classification and localisation; a Clinical Agent extracts structured features including breast density (ACR), view type (CC/MLO), laterality, mass shape, margin, calcification type and distribution, BI-RADS score, pathology status, and subtlety rating utilising optimised tabular learning models; a Risk Assessment Agent integrates outputs from the imaging and clinical agents to produce personalised malignancy predictions; and an Explainability Agent provides role-specific interpretations through Grad-CAM for imaging, SHAP for clinical features, and natural language explanations customised for radiologists, general practitioners, and patients. Predictive dependability is assessed by Expected Calibration Error (ECE) and Brier Score. The framework employs a modular design with a Streamlit interface, facilitating both comprehensive deployment and interactive demonstration. This paradigm enhances the creation of reliable AI systems for clinical decision assistance in oncology by the integration of strong interpretability, personalised risk assessment, and smooth multimodal integration.

Predictive maintenance (PdM) has emerged as a critical enabler for improving reliability, operational efficiency, and regulatory compliance in pharmaceutical manufacturing. Despite its proven effectiveness in several industrial sectors, PdM adoption within the pharmaceutical industry remains comparatively limited due to sector-specific technical, regulatory, and organizational constraints. This paper presents a structured technical analysis of recent academic and industrial works addressing PdM implementation in pharmaceutical manufacturing systems. The analysis examines applied AI and machine learning techniques, sensor and data acquisition strategies, implementation maturity, and regulatory considerations relevant to highly regulated environments. The findings indicate that, while PdM solutions can significantly improve equipment availability and reduce maintenance-related costs, major barriers persist, including limited failure data, validation and re-validation requirements, and organizational resistance to data-driven maintenance practices. Based on this analysis, the paper argues that hybrid approaches combining physics-based models (PBMs) with data-driven methods and explainable artificial intelligence (XAI), supported by digital twins and robust data governance frameworks, represent a practical and regulation-aware pathway for the broader adoption of predictive maintenance in pharmaceutical manufacturing