The large-scale analysis of digital medical data is crucial for advancing personalized healthcare. Textual data, particularly doctor’s letters, serve as key documentation of patient health and encapsulate both expert knowledge and clinical progress. This paper presents the development of an AI-based system for the automated analysis of doctor’s letters, focusing on the recognition and classification of essential medical terms to enhance document clarity and expedite access to critical information for medical staff. The workflow involves pre-processing PDF documents with image processing techniques, extracting text via optical character recognition (OCR), and converting it into a machine-readable format using natural language processing (NLP). Subsequently, AI algorithms such as support vector machines (SVM) and decision trees identify and highlight relevant medical information within the text. Evaluation demonstrates that the model achieves high accuracy in recognizing diagnoses and examinations, with the combination of SVM and named entity recognition (NER) proving especially effective. This approach supports reliable identification of therapies and medical recommendations, contributing to more efficient and interpretable clinical documentation.