Extraction of Endoscopic Markers from Clinical Notes in Italian Patients with Autoimmune Atrophic Gastritis Using Small Language Models

While Large Language Models (LLMs) hold great potential for clinical applications, their use is limited by concerns regarding data privacy, high computational demand, and the risk of hallucinations. Small Language Models (SLMs) are a promising solution, enabling efficient and secure on-device processing. This study presents the application of a local IT5 model finetuned to extract endoscopic markers from Italian annotated clinical notes of patients with Autoimmune Atrophic Gastritis (AAG). The results show that this model performs competitively with both GPT-4o mini-a general-purpose model-and MedGemma-a medical-oriented model-in this specific task, achieving high sensitivity, which is crucial for rare disease detection. These findings highlight the advantages of local, task-specific SLMs for privacy-preserving applications within healthcare settings.