The ever-increasing supply of information combined with the growing knowledge elicitation capabilities of key emerging technologies presents pharmacovigilance with enormous opportunities. Currently, safety monitoring is expanding its evidence base, moving beyond traditional approaches towards sophisticated methods that can identify possible safety signals from multiple information sources, both structured and unstructured. In this context, health information posted online by patients represents a potentially valuable, yet currently left largely unexploited source of post-market safety data that could supplement data from traditional sources of drug safety information. As the use of social media data for pharmacovigilance is still in its infancy, the present paper explores the state of the art in the application of social data to adverse drug reaction detection; provides a thorough review of existing work in the field, highlighting important research efforts and achievements; and finally, discusses the current challenges and promising avenues for future work. Following a literature review methodology, a critical appraisal was conducted of carefully selected work on the use of social data in post-market surveillance, as presented in the recent scientific literature. Out of a sample of more than 1300 articles, which was the result of the literature search, the final selection of articles was made based on their relevance to the applications of social networking sites (SNS) to pharmacovigilance, and a thorough review of this corpus was completed with a total of 100 articles reviewed. The main contributions of this review include the mapping and systematisation of the current knowledge in the field by drawing comparisons of different approaches, types of social data and of relevant sources currently used in the field, and by developing new classifications of social data sources and taxonomies for social data for use in pharmacovigilance, as well as the identification of key challenges and the extraction of new insights in terms of potential for practical applications and future research directions in the area of pharmacovigilance.

This paper introduces a novel framework for addressing domain adaptation challenges in large language models (LLMs), emphasising privacy-preserving synthetic data generation and efficient fine-tuning. The proposed framework employs a multi-stage approach that includes document ingestion, relevance assessment, and automated dataset creation. This process reduces the need for extensive technical expertise while safeguarding data privacy. We evaluate the framework’s performance on domain-specific tasks in fields such as biobanking and public health, demonstrating that models fine-tuned using our method achieve results comparable to larger proprietary models. Crucially, these models maintain their general instruction-following capabilities, even when adapted to specialised domains, as shown through experiments with 7B and 8B parameter LLMs. Key components of the framework include continuous pre-training, supervised fine-tuning (SFT), and reinforcement learning methods such as direct preference optimisation (DPO), which together provide a flexible and configurable solution for deploying LLMs. The framework supports both local models and API-based solutions, making it scalable and accessible. By enabling privacy-preserving, domain-specific adaptation without requiring extensive expertise, this framework represents a significant step forward in the deployment of LLMs for specialised applications. The framework significantly lowers the barrier to domain adaptation for small- and medium-sized enterprises (SMEs), enabling them to utilise the power of LLMs without requiring extensive resources or technical expertise.

Immersive technologies are gaining momentum in professional training. Advanced AR/VR and gaming technologies allow for authentic learning experiences that are close to real‑life conditions, problems, and applications, something that can revolutionise fields like medical training, which traditionally relied on textbook‑based theoretical education and clinical placements. The present paper discusses the application of advanced gaming and immersion technologies for the continuous training of physiotherapists. Presently, this professional group remains largely underserved by online training services. The paper argues that the inherent complexity of physical therapy training could be served effectively by immersive learning innovations and professionals could benefit greatly. The paper applies the Design Science Research Methodology (DSRM) to design a suitable training solution for the promotion of authentic, comprehensive learning. Our analysis targets and consolidates the findings from three distinct pillars: (a) the requirements of the application area in question; (b) the current state‑of‑the‑art and emerging directions in game‑based professional education and training; and (c) existing applications of game‑based learning in the field of healthcare. Subsequently, the paper consolidates the findings to produce the system requirements and presents the design of a prototype training environment for physiotherapists that builds on advanced gaming technologies. Within the proposed system design, augmented reality modules are embedded alongside the game simulation and virtual world environment to provide participants with an authentic simulated game scenario in which to immerse themselves.

The increasing pervasiveness of digital technology is having a profound effect on how younger generations interact, play, and learn. The use of electronic games for education (game-based learning) promotes an agile, immersive, and stimulating form of learning that fosters learner engagement and motivation. This chapter focuses on GBL for knowledge sharing and transfer for the purposes of intergenerational learning. In addition to the challenge of building intellectually appropriate and challenging games, the informal and incidental nature of intergenerational learning introduces another key requirement: namely, that of effectively capturing and re-proposing the informal knowledge of seniors. For this reason, an innovative approach that builds on the combined use of serious games and storytelling is proposed. The application of this methodology in the context of the research project e-VITA is discussed, including the implications of pedagogy upon game design.

The present chapter investigates the emerging paradigm of cultural heritage experience, as shaped by the continuous advances in information technologies. Recent years have seen the growing digitisation of cultural heritage, leveraged by innovative information technologies (imaging technologies, multimedia, virtual reality etc.). Advanced digitisation, and digital preservation and accessibility have been instrumental in transforming conservation and scientific research methods in the field of cultural heritage, as well as people’s experience of cultural heritage assets, relics, and monuments. Digitisation and immersion technologies are already in use in the context of cultural tourism in museums and on location. At the same time, a manifold of new applications and services can be generated from the adoption and adaptation of relevant technologies already applied in other sectors (e.g. 2D/3D digital scanning technologies applied in the construction industry). The present chapter will provide a thorough review of relevant digital technologies and existing work in the field, highlighting important research efforts and achievements; and will discuss the current challenges and promising avenues for future work. Following a literature review methodology, our research will provide a critical appraisal of carefully selected work from recent scientific literature and contribute to the systematisation of the current knowledge in the field towards the identification of key challenges and the extraction of new insights in terms of potential for practical applications and future research directions in the area.