Abstract
Federated learning offers a powerful framework for machine learning in medical imaging by enabling collaborative model training across decentralised institutions while preserving patient privacy. However, practical deployment faces challenges arising from data scarcity and heterogeneity, which can substantially impair model performance. This review systematically examines the diverse forms of heterogeneity in federated medical imaging, categorising them into feature skew, label skew, quantity skew, and quality skew. It also distinguishes data scarcity and label scarcity as separate but related challenges. By analysing more than 130 recent publications, the review surveys state-of-the-art algorithms designed to mitigate these problems, classifying them by methodology-including model architecture optimisation, aggregation strategies, and personalisation approaches. This review offers researchers aiming to develop federated learning systems for medical image analysis a comprehensive overview of the major challenges posed by different skews and scarcities, along with the range of solutions proposed to address them. As federated learning moves from research settings to clinical practice, understanding and addressing data heterogeneity and scarcity will be essential to ensuring that collaborative learning systems perform reliably across the diversity of real-world medical imaging environments.

Abstract
The integration of AI into organizational settings leads to a growing need for hybrid human-AI collaborative approaches, necessary due to the increasing autonomy, impact and responsibility AI-based solutions have. Moreover, to ensure a sustainable integration into existing processes, such approaches must be context-aware, transparent, and human-centric. In line with the Industry 5.0 paradigm, this paper presents a novel Multi-Agent System architecture that enables meaningful collaboration between human and artificial agents through a socio-technical design approach. The proposed architecture is grounded in a structured, real-time context stream derived from organizational data sources, which semantically describe human actors, processes, and industrial resources. Central to this system is a set of four core LLM-based agents, each responsible for orchestrating hybrid human-AI tasks along distinct dimensions of timing, role selection, resource allocation, and execution sequencing. To assess the feasibility and effectiveness of the architecture, we report on an early-stage validation conducted within a representative industrial use case in the automotive sector, focused on information retrieval. In this use case, the architecture was tasked with answering a set of representative, domain-specific questions by dynamically interacting with distributed industrial databases. Results demonstrate the architecture's ability to coordinate relevant human and artificial agents, retrieve semantically-relevant data, and present explainable outputs, showcasing its potential for supporting decision-making processes in hybrid collaborative networks.

Abstract
The automotive sector is undergoing continuous technological evolution driven by the demand for sustainable and safe vehicles. Among the main factors influencing safety, driver behaviour has been identified as a critical contributor to road crashes. This systematic review explores recent innovations in detecting risky driver behaviours, addressing six research questions: the most relevant datasets used for algorithm development and evaluation; system architectures and methodologies for anomaly detection; the most studied driver behaviours and related environmental, human, and mechanical factors; advances in machine learning, deep learning, and statistical methods; performance metrics and validation approaches; and the role of embedded technologies and sensors in practical applications. The review included 93 peer-reviewed articles published between 2020 and 2024, sourced from ACM, IEEE, ScienceDirect, and Scopus. Exclusion criteria were duplicates, non-open access, retracted works, and studies unrelated to outlier detection or driver behaviour. The Parsifal tool was used to support systematic data processing. Results highlight the most frequently used datasets, proposed models, and their performance in detecting driver behaviours, as well as the influence of contextual factors such as traffic rules, road conditions, and sensor limitations. Despite advances, real-world integration remains challenging, requiring further research and development. This review aims to guide researchers in understanding the current state of anomaly detection in driving contexts and to emphasize the need for broader collaboration to create effective, deployable solutions that enhance road safety worldwide.

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Abstract

Abstract
Artificial Intelligence (AI) integration in supply chain systems is growing, and with it grows its potential impact on inter-organisational collaborative networks. We review existing literature on how different AI archetypes (Reflexive, Anticipatory, Supervisory, Prescriptive) could support Collaborative Supply Chain Management (CSCM) activities, and how they impact information sharing, collaborative decision-making, and trust among supply chain partners at different integration levels. Adopting a sociotechnical perspective, we synthesise existing literature and map the archetypes along four levels of AI integration, varying in scope and decision autonomy. The results are conceptual frameworks demonstrating how AI impacts collaboration dynamics as it evolves from a decision-support tool to an autonomous coordination agent. Findings show differentiated effects along archetypes and integration levels, with implications for CSCM governance, transparency, and resilience. We contribute to the discussion on human-AI collaboration in CSCM and offer a baseline for research on the human-centric values of Industry 5.0.