A Review of Approaches to Standardizing Medical Descriptions for Clinical Entity Recognition: Implications for Artificial Intelligence Implementation
This article reviews the current state of standardization in specific areas of the medical sector in Poland, focusing on terminology and the unique context of the Polish language. The primary objective was to analyze the existing resources and examine possibilities, challenges, and opportunities ass...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/21/9903 |
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| Summary: | This article reviews the current state of standardization in specific areas of the medical sector in Poland, focusing on terminology and the unique context of the Polish language. The primary objective was to analyze the existing resources and examine possibilities, challenges, and opportunities associated with integrating Artificial Intelligence, particularly natural language processing methods, into the healthcare system. The additional goal of this review was to place Poland in the international context by comparing the current state of the Polish standardization of healthcare with those of selected countries with more and less developed systems. The exploration highlights the main challenges that impact integration, including the specificity of the language and challenges in transferring knowledge from other languages, lack of communication between parties, and lack of stakeholder involvement in the standardization processes. This review also presents potential solutions to the mentioned challenges and provides insights into future directions, possibilities, proposals, and recommendations for all stakeholders. The practical application of this research extends beyond Poland. Many countries with underrepresented languages face similar challenges in clinical data processing, and the advances in CER for Polish could serve as a model for implementing AI-driven solutions in these regions. By refining CER models and adapting them to diverse linguistic and healthcare contexts, this research can foster improvements in patient care, medical research, and healthcare administration on a global scale. |
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| ISSN: | 2076-3417 |