Organizations in healthcare are increasingly adopting NLP to process vast amounts of clinical data, which assists in informed decision-making. However, the success of NLP largely depends on the training of large language models (LLMs) that uphold semantic interoperability—ensuring that clinical concepts retain their meaning and intent when transmitted. Achieving this requires LLMs to be meticulously trained on structured clinical terminology to avoid data quality loss, which could impact various healthcare processes negatively. The paper discusses the critical role of semantic interoperability in NLP and the complexities of training LLMs for effective use in healthcare.

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