Abstract
In healthcare, diagnoses usually rely on physician expertise. However, complex cases may benefit from consulting similar past clinical reports cases. In this paper, we present MedLink (http://medlink.inesctec.pt), a tool that given a free-text medical report, retrieves and ranks relevant clinical case reports published in health conferences and journals, aiming to support clinical decision-making, particularly in challenging or complex diagnoses. To this regard, we trained two BERT models on the sentence similarity task: a bi-encoder for retrieval and a cross-encoder for reranking. To evaluate our approach, we used 10 medical reports and asked a physician to rank the top 10 most relevant published case reports for each one. Our results show that MedLink’s ranking model achieved NDCG@10 of 0.747. Our demo also includes the visualization of clinical entities (using a NER model) and the production of a textual explanation (using a LLM) to ease comparison and contrasting between reports. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
In the spring of 2023, a team of European Project Semester (EPS) students enrolled at the Instituto Superior de Engenharia do Porto (ISEP) chose to foster socialisation in urban spaces. Public spaces are ideal sites to promote social interaction and community involvement. The aim of this project is then to use such places to divert attention from smartphones by promoting physical social interaction. In recent years, the combination of interactive games and technology has emerged as a potential strategy to increase the use and allure of public areas. The proposed solution, named Shift it, is a puzzle game that combines technology with old school gaming, providing a fun and unique socialising experience. The game, to be installed in public areas, has as key features inclusiveness (invites all people to play), fun (creates a healthy competitive setup) and empathy (creates puzzles by taking and scrambling user pictures). This paper presents the proposed design, which was based on state-of-the-art, ethics, market and sustainability analyses, followed by the development and testing of a proof-of-concept prototype. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Abstract
The Iliad Digital Twins of the Ocean is a European Green Deal Project which aims at the development of an architecture and set of components, tools and services for the creation of digital twins of the ocean. The approach aims to support the emerging European Digital Twins of the Ocean (EDITO) initative with associated projects like EDITO Infra and EDITO Model lab and the overall Destination Earth (DestinE) initiative and also taking advantage of the evolving European Common Data Spaces including the Green Deal Data Space, the Copernicus Data Space and the EOSC cross domain Data Space. The paper presents the final version of the Iliad digital twin interoperability architecture based on four steps of a digital twin pipeline from Data Acquisition/Collection to Digital Twin Data Representation to Digital Twin Hybrid and Cognitive/AI Analytics Models and further to Digital Twin Visualisation and Control, which are presented together with associated Digital twin components and services.

Abstract
Background The Endoscopic Grading of Gastric Intestinal Metaplasia (EGGIM) classification correlates with histological assessment of gastric intestinal metaplasia and enables stratification of gastric cancer risk. We developed and evaluated an artificial intelligence (AI) approach for EGGIM estimation. Methods Two datasets (A and B) with 1280 narrow-band imaging images were used for per-image analysis. Still images with manually selected patches of 224 x 224 pixels, annotated by experts, were used. Dataset A was retrospectively collected from clinical routine; Dataset B (used for per-patient analysis) was prospectively collected and included 65 fully documented patients. To mimic clinical practice, a deep neural network classified image patches into three EGGIM classes (0, 1, 2) and calculated the total per-patient EGGIM score (0-10). Results On per-image analysis, an accuracy of 87% (95%CI 71%-100%) was obtained. Per-patient EGGIM estimation had an average error of 1.15 (out of 10) and showed 88% (95%CI 80%-96%) accurate clinical decisions for surveillance (EGGIM >= 5), with 85% (95%CI 75%-94%) specificity, no false negatives, and positive and negative predictive values of 62% (95%CI 32%-92%) and 100% (95%CI 100%-100%), respectively. Conclusions EGGIM was estimated with high accuracy using AI tools in endoscopic image analyses. Automated assessment of EGGIM may provide a greener strategy for gastric cancer risk stratification, prospective studies, and interventional trials.

Abstract
Social media platforms, increasingly used as news sources for varied data analytics, have transformed how information is generated and disseminated. However, the unverified nature of this content raises concerns about trustworthiness and accuracy, potentially negatively impacting readers' critical judgment due to disinformation. This work aims to contribute to the automatic data quality validation field, addressing the rapid growth of online content on wiki pages. Our scalable solution includes stream-based data processing with feature engineering, feature analysis and selection, stream-based classification, and real-time explanation of prediction outcomes. The explainability dashboard is designed for the general public, who may need more specialized knowledge to interpret the model's prediction. Experimental results on two datasets attain approximately 90% values across all evaluation metrics, demonstrating robust and competitive performance compared to works in the literature. In summary, the system assists editors by reducing their effort and time in detecting disinformation.

Abstract