Misinformation in healthcare, from vaccine hesitancy to unproven treatments, poses risks to public health and trust in medical systems. While machine learning and natural language processing have advanced automated fact-checking, validating biomedical claims remains uniquely challenging due to complex terminology, the need for domain expertise, and the critical importance of grounding in scientific evidence. We introduce CER (Combining Evidence and Reasoning), a novel framework for biomedical fact-checking that integrates scientific evidence retrieval, reasoning via large language models, and supervised veracity prediction. By integrating the text-generation capabilities of large language models with advanced retrieval techniques for high-quality biomedical scientific evidence, CER effectively mitigates the risk of hallucinations, ensuring that generated outputs are grounded in verifiable, evidence-based sources. Evaluations on expert-annotated datasets (HealthFC, BioASQ-7b, SciFact) demonstrate state-of-the-art performance and promising cross-dataset generalization. Code and data are released for transparency and reproducibility: https://github.com/PRAISELab-PicusLab/CER

Biomedical misinformation - ranging from misleading social media posts to fake news articles and deepfake videos - is increasingly pervasive across digital platforms, posing significant risks to public health and clinical decision-making. We developed CER, a comprehensive fact-checking system designed specifically for biomedical content. CER integrates specialized components for claim detection, scientific evidence retrieval, and veracity assessment, leveraging both transformer models and large language models to process textual and multimedia content. Unlike existing fact-checking systems that focus solely on structured text, CER can analyze claims from diverse sources including videos and web content through automatic transcription and text extraction. The system interfaces with PubMed for evidence retrieval, employing both sparse and dense retrieval methods to gather relevant scientific literature. Our evaluations on standard benchmarks including HealthFC, BioASQ-7, and SciFact demonstrate that CER achieves state-of-the-art performance, with F1-score improvements compared to existing approaches. To ensure reproducibility and transparency, we release the GitHub repository with the source code, within which you can reach an interactive demonstration of the system published on HuggingFace and a video demonstration of the system \https://github.com/PRAISELab-PicusLab/CER-Fact-Checking.

Player scouting in soccer is witnessing a surge of interest from the research community. Traditional scouting methods are often limited by subjectivity and biases in evaluation. Moreover, the lack of structured data and models hinders the progress of the field. To overcome these limitations, we introduce a novel player recommendation system which integrates similarity techniques and generative artificial intelligence. It aims to support player recruitment by providing a data-driven and inclusive approach. The novelty of our work lies in its use of advanced machine learning and artificial intelligence to accurately predict player potential and performance by similarity measures, thereby mitigating the influence of subjective biases that often affect talent identification. Our contributions represent a significant advancement in the field of sports analytics and talent identification, offering a more equitable and efficient approach to scouting and recruitment. The results obtained underscore the effectiveness of the proposed system, demonstrating the transformative potential of artificial intelligence in revolutionizing talent scouting.

Geopolitical news provides vast amounts of information essential for understanding international relations and political events. However, organizing this information into a coherent, structured format poses challenges due to the complexity and dynamic nature of the domain. This paper introduces a scalable system leveraging Large Language Models to build continuously updated Knowledge Graphs from Italian geopolitical news. The system features a modular architecture, including a Collector Node for scalable article extraction, a Redis-based reliable queue to manage large-scale data ingestion, and a Named Entity Recognition/Relation Extraction Engine to standardize entity-relation triples. The framework addresses key challenges, such as continuous updating and hallucination mitigation, ensuring the reliability of the graph. Our evaluations demonstrate significant improvements in scalability, uniformity of extracted triples, and graph accuracy, making this architecture particularly suitable for real-time geopolitical analysis.

Legal Artificial Intelligence has emerged as an essential field, focusing on AI technologies that facilitate various legal tasks and alleviate the workload of legal professionals. Despite advancements in Legal Artificial Intelligence, there remains a critical gap in systems that can provide both comprehensive and contextually accurate retrieval tailored to the intricate structure of EU legislation. We propose EuropeanLawAdvisor, an efficient and user-friendly legal information retrieval system designed to deliver tailored responses to legal queries. This system utilizes open-source Large Language Models within a Retrieval-Augmented Generation framework, facilitating precise and relevant information retrieval. The system employs a robust retrieval approach that integrates multi-match, k-nearest neighbors, hybrid methods, and TF-IDF search strategies across both complete documents and segmented text indexes, ensuring comprehensive retrieval for diverse query types. The implementation of the framework has demonstrated significant improvements in the accuracy and relevance of responses to EU legal queries, enhancing both the retrieval of relevant legal documents and the generation of precise responses. We show that EuropeanLawAdvisor, leveraging open-source models like Phi3-mini-3B and LLaMa-3-8B, achieves competitive Faithfulness and Relevance compared to GPT-4-Turbo. The performance gap narrows significantly in zero-shot scenarios, and our approach outperforms GPT-4-Turbo in the percentage of answered questions. We publicly release our code on GitHub: https://github.com/raffaele-russo/EuropeanLawAdvisor.