Vision-Language Models (VLMs) excel in multimodal tasks, yet their effectiveness in specialized medical applications remains underexplored. Accurate interpretation of medical images and text is crucial for clinical decision support, particularly in multiple-choice question answering (MCQA). To address the lack of benchmarks in this domain, we introduce the Multilingual Multimodal Medical Exam Dataset (MMMED), designed to assess VLMs' ability to integrate visual and textual information for medical reasoning. MMMED includes 582 MCQA pairs from Spanish medical residency exams (MIR), with multilingual support (Spanish, English, Italian) and paired medical images. We benchmark state-of-theart VLMs, analyzing their strengths and limitations across languages and modalities. The dataset is publicly available on Hugging Face (https://huggingface.co/datasets/praiselab-picuslab/MMMED), with experimental code on GitHub (https://github.com/PRAISELab-PicusLab/MMMED).

Medical question answering systems face deployment challenges including hallucinations, bias, computational demands, privacy concerns, and the need for specialized expertise across diverse domains. Here, we present SOLVE-Med, a multi-agent architecture combining domain-specialized small language models for complex medical queries. The system employs a Router Agent for dynamic specialist selection, ten specialized models (1B parameters each) fine-tuned on specific medical domains, and an Orchestrator Agent that synthesizes responses. Evaluated on Italian medical forum data across ten specialties, SOLVE-Med achieves superior performance with ROUGE-1 of 0.301 and BERTScore F1 of 0.697, outperforming standalone models up to 14B parameters while enabling local deployment. Our code is publicly available on GitHub: https://github.com/PRAISELab-PicusLab/SOLVE-Med.

The rapid rise of deep generative models has enabled realistic synthetic media, posing severe risks to healthcare communication where trust and accuracy are essential. Existing deepfake detectors, mostly trained on entertainment-oriented datasets, degrade sharply in medical contexts, where domainspecific terminology and evidence-based discourse are critical. Moreover, post-hoc moderation is often too slow to mitigate the spread of health-related disinformation, underscoring the need for timely, domain-adapted solutions. We address these gaps by (i) curating a multimodal healthcare-oriented corpus of in-the wild and synthetically generated deepfakes, (ii) benchmarking state-of-the-art monomodal detectors for video, audio, and text, and (iii) introducing a late-fusion framework that integrates their predictions via ensemble methods. Our experiments show that late fusion consistently outperforms individual detectors, yielding notable gains in macro-F1 and AUC (+0.159 and +0.131, respectively), particularly under domain-shift and lowresource conditions. These findings demonstrate the importance of multimodal integration for robust detection of health-related deepfakes and contribute toward building trustworthy AI systems to safeguard medical communication. Code and data will be released for transparency and reproducibility: https://github.com/PRAISELab-PicusLab/Deepfake-Detection-in-Healthcare

The increasing volume of unstructured clinical data—such as consultation audio, notes, and reports—has intensified the documentation burden on physicians, limiting time for direct patient interaction. Existing transcription and summarization systems remain largely proprietary and opaque, offering limited semantic interpretability or clinical control. We present AMICO (Anamnesi Medica Intelligente del Colloquio Ospedaliero), a multimodal framework for AI-assisted clinical documentation that integrates audio acquisition, semantic processing, and interactive human validation. The system combines automatic speech recognition, medical named entity recognition, summarization, and drug-drug interaction checking within a unified workflow designed to support, not replace, physician expertise. Evaluation shows that a fine-tuned LLM achieves superior accuracy and faithfulness compared to baseline LLMs, confirming the effectiveness of domain-specific adaptation for Italian medical dialogue summarization. AMICO demonstrates that moderately sized, transparent, and human-supervised models can meaningfully reduce documentation workload while ensuring clinical reliability and safety.

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.