## 5 Discussion
### Limitations and Future Research Directions  
This article faced several limitations and evident risks of bias that could have impacted its findings and recommendations. One notable limitation was the reliance on a relatively narrow range of sources, especially publications available in Polish, which were not always indexed in international databases like PubMed or Scopus. This limited the scope of the review and introduced the risk of selection bias, as the studies reviewed might not have fully represented the global state of research in CER and medical standardization. The scarcity of English-language studies on the subject may have hindered cross-country comparisons and the generalizability of the findings to other healthcare systems.  
The lack of widely available, well-annotated datasets for Polish-language medical texts was another significant limitation that affected the conclusions drawn about CER's applicability in Poland. This limitation hampered the development of AI models and introduced a bias, as the performance of CER systems tested on limited data may not have reflected their real-world potential in diverse clinical settings. Furthermore, this article's emphasis on the Polish context may have obscured broader international challenges, limiting its applicability to other healthcare systems that faced different regulatory or linguistic issues.  
Additionally, the methodology used in this review, although suitable for exploring broad trends, lacked the rigorous assessment of quality and bias that systematic reviews typically provide. This could have led to the inclusion of studies with varying degrees of reliability, potentially affecting the accuracy of the conclusions.  
The findings of this study could guide further activities in similar linguistic and healthcare contexts. However, the evident risk of bias arising from the selection of non-indexed, localized research emphasizes the need for more collaborative, international studies that could address these limitations.  
Future research on CER in Polish should prioritize several key directions. The creation of comprehensive and well-annotated datasets is essential. Collaboration among healthcare professionals, linguists, and AI experts will help ensure that these datasets capture the complexities of Polish clinical texts, supporting the development of more accurate machinelearning models. Partnerships with healthcare institutions are crucial for accessing clinical records, enabling the curation of larger, more diverse datasets that reflect real-world clinical scenarios.  
Another primary focus is the standardization of annotation guidelines. Consistent labeling rules will enhance the quality and interoperability of datasets, facilitating more effective machine learning applications. Moreover, open data initiatives that ensure patient privacy while sharing these annotated datasets should be encouraged to accelerate research.  
International collaboration also presents a significant opportunity. Engaging with global research communities facing similar challenges in other languages can provide valuable insights and shared strategies, helping Polish CER models progress more efficiently. Lastly, educational campaigns are needed to raise awareness about the importance of annotated datasets for AI-driven medical solutions, which can help secure broader support for future research initiatives.  
These conclusions are expanded upon by recommendations presented in the next section, which can be generalized to health systems in other countries.