This study enhances and applies a methodology for terminology monitoring, originally developed by Montané March and Cabré, to the field of psychiatry. By analyzing the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and its text revision (DSM-5-TR), the research identifies 31 neologisms, primarily formed through morphological and complete neology. The methodology involves six stages: corpus retrieval, data preprocessing, automated comparison of analytical indexes, identification of neological term candidates, expert revision, and the proposal of Italian equivalents. Significant enhancements include automating the comparison phase using the Python programming language, which streamlines the process, reduces manual effort, and minimizes errors. This work not only refines the original approach but also contributes to a deeper understanding of how new terms emerge in psychiatry, offering a valuable framework for future terminology monitoring across specialized languages.