"Development of an agentic AI for processing electronic health records for stroke rehabilitation" Stroke rehabilitation represents a critical phase in patient care, where timely access to reliable and personalized information can significantly influence clinical outcomes. In most modern healthcare systems, patient information is stored in electronic health records (EHRs), which clinicians rely on as the primary source for medical history, monitoring progress, and planning treatments. However, these records are often heterogeneous, unstructured, and difficult to process automatically, creating barriers to their effective use in supporting individualized rehabilitation plans. This thesis addresses this challenge by proposing the development of an agentic artificial intelligence (AI) system specifically designed for the extraction, structuring, and summarization of clinical information from EHRs.