{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:03:50Z","timestamp":1755219830232,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686080","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"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":[[2025,8,7]]},"abstract":"<jats:p>This study investigated the application of Large Language Models (LLMs) for simplifying biomedical texts to enhance health literacy. Using a public dataset, which included plain language adaptations of biomedical abstracts, we developed and evaluated several approaches, specifically a baseline approach using a prompt template, a two AI agent approach, and a fine-tuning approach. We selected OpenAI gpt-4o and gpt-4o mini models as baselines for further research. We evaluated our approaches with quantitative metrics, such as Flesch-Kincaid grade level, SMOG Index, SARI, and BERTScore, G-Eval, as well as with qualitative metric, more precisely 5-point Likert scales for simplicity, accuracy, completeness, brevity. Results showed a superior performance of gpt-4o-mini and an underperformance of FT approaches. G-Eval, a LLM based quantitative metric, showed promising results, ranking the approaches similarly as the qualitative metric.<\/jats:p>","DOI":"10.3233\/shti250946","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:36:04Z","timestamp":1754566564000},"source":"Crossref","is-referenced-by-count":0,"title":["Plain Language Adaptations of Biomedical Text Using LLMs: Comparision of Evaluation Metrics"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9064-5085","authenticated-orcid":false,"given":"Primoz","family":"Kocbek","sequence":"first","affiliation":[{"name":"University of Maribor, Faculty of Health Sciences, Maribor, Slovenia"},{"name":"University of Ljubljana, Faculty of Medicine, Ljubljana, Slovenia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6647-9988","authenticated-orcid":false,"given":"Leon","family":"Kopitar","sequence":"additional","affiliation":[{"name":"University of Maribor, Faculty of Health Sciences, Maribor, Slovenia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0183-8679","authenticated-orcid":false,"given":"Gregor","family":"Stiglic","sequence":"additional","affiliation":[{"name":"University of Maribor, Faculty of Health Sciences, Maribor, Slovenia"},{"name":"University of Edinburgh, Usher Institute, Edinburgh, UK"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI250946","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:36:04Z","timestamp":1754566564000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250946"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250946","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}