This paper describes the system submitted by our team to the MultiClinAI shared task at the 11th SMM4H-HeaRD Workshop (ACL 2026). The task addresses multilingual clinical Named Entity Recognition (NER) for three entity types (Disease, Procedure, and Symptom) in Spanish clinical texts. Our approach fine-tunes MrBERT-biomed, a domain-adapted ModernBERT model pre-trained on biomedical corpora, using multilingual clinical data from seven European languages. We train independent entity-specific models, each optimized via Bayesian hyperparameter search with Optuna, and apply a deterministic post-processing step that aligns predicted spans to word boundaries. On the official test set, our system achieves overall strict micro-F1 scores of 0.7453, 0.7107, and 0.6603 for Disease, Procedure, and Symptom, respectively.

Discharge summaries are critical documents for continuity of care, yet their manual creation imposes significant burdens on clinical staff. This systematic literature review examines current approaches to automatic generation of discharge summaries using Natural Language Processing (NLP) and Large Language Models (LLMs). Following the Kitchenham guidelines for systematic reviews in software engineering, we searched Scopus and PubMed databases for studies published between 2023 and 2026, identifying 9 primary studies from an initial pool of 102 papers. Our analysis reveals that GPT-4 and its variants dominate current research (appearing in 6 of 9 studies), while open-source alternatives like LLaMA show promise for privacy-preserving deployments. Evaluation primarily relies on automatic metrics (ROUGE, BLEU) combined with human expert assessment. Key challenges include hallucination rates ranging from 33% to 64%, information omission, integration with Electronic Health Record (EHR) systems, and context window limitations. Studies addressing factuality employ human-in-the-loop validation, prompt engineering techniques, and knowledge graph-based correction mechanisms. Despite these challenges, recent implementations demonstrate clinical feasibility, with one study achieving a 94.35% System Usability Score. This review provides a comprehensive synthesis of the state-of-the-art and identifies opportunities for future research in this rapidly evolving field.

Medical discharge reports frequently contain highly technical language that creates significant communication barriers between healthcare professionals and patients, potentially compromising treatment adherence and post-discharge care quality. In this paper, we present SimpliMED, a modular system designed to automatically simplify cardiology discharge reports using Large Language Models (LLMs) and advanced Natural Language Processing techniques (NLP). Our architecture integrates section-based preprocessing with specialized prompts, explicit handling of medical abbreviations, and therapeutic explanations of medications to enhance accessibility. We evaluate our system using a corpus of 307 anonymized cardiology discharge reports from a Spanish medical center. For abbreviation detection, our fine-tuned Small Language Model (SLM) achieves an F1-score of 0.90, significantly outperforming regex-based approaches (F1: 0.67). For medication recognition, we achieve F1-scores of 0.91 for commercial names and 0.70 for active principles. We also contribute a therapeutic dictionary containing 14,611 medications with patient-friendly explanations extracted from the Spanish Agency of Medicines. Expert evaluation by two cardiologists yields an overall quality score of 75%, with highest performance for admission reason (91%) and current illness (75%) sections. While results demonstrate the potential of LLM-based medical text simplification for Spanish clinical language, we identify areas requiring further development before clinical deployment.