## 1 Introduction
- How can standardization efforts in medical descriptions improve the effectiveness of CER in the Polish healthcare context? - What are the gaps and challenges in developing CER systems tailored to the Polish language? - What are the standardization directions in Polish healthcare systems? * Source selection. The source selection process involved a combination of local and international databases to ensure the broadest possible inclusion of relevant studies: Polish industry journal databases, international databases (Google Scholar, PubMed, and Scopus), and local archives. * Studies specifically focused on Polish-language medical texts or methodologies for recognizing clinical entities in Polish;
- Articles that discussed the standardization of medical descriptions in Polish, including terminologies, controlled vocabularies, and ontologies;
- Research that explored the development of AI-based CER systems or similar natural language processing (NLP) technologies in Polish healthcare contexts;
Works that have been published in the last twenty years. * Exclusion criteria. To maintain the focus on relevant research and avoid non-applicable studies, certain exclusion criteria were applied:
* Articles that focused on general-purpose Name Entity Recognition methods without applications to medical texts or the Polish language were excluded;
* Studies that did not explore the specific context of Polish healthcare or were only tangentially related to the standardization of medical data;
* Publications with outdated methodologies or insufficient empirical data were excluded to maintain the review's quality. * Data analysis and synthesis. The articles chosen were analyzed in depth to extract critical insights regarding methodologies, challenges, and results in implementing CER systems. The synthesis focused on understanding CER's technical aspects and the broader implications of medical description standardization in Poland. This analysis provided a clearer view of the existing landscape and identified areas where future research is necessary, especially in underexplored areas like Polish-language medical text annotation and the creation of annotated datasets. This article begins with an overview of the transformation of the healthcare system in Poland, providing a background on the current state and evolution of healthcare policies and infrastructure. The following section focuses on CER and other NLP methods for the Polish language, discussing relevant studies and previous works in the area. This is followed by a comprehensive look at challenges and barriers that impede the adoption of NLP and AI technologies in Polish healthcare, highlighting issues like data availability and regulatory limitations. The \"Discussion\" section presents new proposals and possibilities for overcoming these challenges for Polish and other countries' healthcare systems, including a strategy for the coming years, emphasizing the potential of Large Language Models (LLMs). It also explores future directions for research and development in the field. The \"Recommendations\" section provides actionable steps to foster innovation, collaboration, and data standardization, leading into the \"Conclusions\", which summarizes the essential findings and their implications for the future of healthcare technology.