  In the rapidly evolving field of healthcare, the integration of artificial
intelligence (AI) has become a pivotal component in the automation of clinical
workflows, ushering in a new era of efficiency and accuracy. This study focuses
on the transformative capabilities of the fine-tuned KoELECTRA model in
comparison to the GPT-4 model, aiming to facilitate automated information
extraction from thyroid operation narratives. The current research landscape is
dominated by traditional methods heavily reliant on regular expressions, which
often face challenges in processing free-style text formats containing critical
details of operation records, including frozen biopsy reports. Addressing this,
the study leverages advanced natural language processing (NLP) techniques to
foster a paradigm shift towards more sophisticated data processing systems.
Through this comparative study, we aspire to unveil a more streamlined,
precise, and efficient approach to document processing in the healthcare
domain, potentially revolutionizing the way medical data is handled and
analyzed.
