{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T19:55:02Z","timestamp":1784750102341,"version":"3.55.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685960","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,15]]},"abstract":"<jats:p>Standardizing medical terminology is critical for healthcare informatics, particularly for improving data interoperability and patient management systems. This study evaluated four distinct GPT-4-based approaches for mapping local medical terminologies to SNOMED CT: baseline, prompt-engineered, fine-tuned, and Retrieval-Augmented Generation (RAG). Using 1,200 diagnostic terms from a Korean hospital, we assessed the models\u2019 accuracy and error rates. The RAG model achieved the highest performance with a 96.2% valid SNOMED CT term match rate and a 57.6% overall exact match rate, surpassing the fine-tuned model (69.2% valid term match, 47.2% exact match). Error analysis showed that the RAG model also minimized structural errors to 14%, significantly lower than other models. While the fine-tuned and RAG models struggled with specificity, they showed promise for improving automated mapping and Named Entity Recognition (NER) tasks in clinical settings. This study highlights the potential of AI-human collaboration for enhancing autocoding and data standardization in healthcare. Further research is needed to refine specificity and validate these systems for clinical use.<\/jats:p>","DOI":"10.3233\/shti250472","type":"book-chapter","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T08:57:52Z","timestamp":1747385872000},"source":"Crossref","is-referenced-by-count":2,"title":["Comparative Analysis of ChatGPT-4 for Automated Mapping of Local Medical Terminologies to SNOMED CT"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8817-2004","authenticated-orcid":false,"given":"Sookyung","family":"Huh","sequence":"first","affiliation":[{"name":"Department of medical recorder\u2019s team, Severance Hospital, Seoul, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Intelligent Health Systems \u2013 From Technology to Data and Knowledge"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI250472","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T08:57:52Z","timestamp":1747385872000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250472"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,15]]},"ISBN":["9781643685960"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250472","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,15]]}}}