{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["27"],"submitter":["Dennstadt F"],"pubmed_abstract":["<h4>Background</h4>Large language models (LLMs) hold promise for supporting clinical tasks, particularly in data-driven and technical disciplines such as radiation oncology. While prior evaluation studies have focused on examination-style settings for evaluating LLMs, their performance in real-life clinical scenarios remains unclear. In the future, LLMs might be used as general AI assistants to answer questions arising in clinical practice. It is unclear how well a modern LLM, locally executed within the infrastructure of a hospital, would answer such questions compared with clinical experts.<h4>Objective</h4>This study aimed to assess the performance of a locally deployed, state-of-the-art medical LLM in answering real-world clinical questions in radiation oncology compared with clinical "],"journal":["Journal of medical Internet research"],"pagination":["e69752"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12504895"],"repository":["biostudies-literature"],"pubmed_title":["Comparative Evaluation of a Medical Large Language Model in Answering Real-World Radiation Oncology Questions: Multicenter Observational Study."],"pmcid":["PMC12504895"],"pubmed_authors":["Windisch P","Haddad H","Bachmann N","Ahmadsei M","Aebersold DM","Gkika E","Hastings J","Putora PM","Dennstadt F","Rogers S","Glatzer M","Hoefler D","Schmerder M","Sinovcic D","Bryjova K","Maffei M","Christ SM","Mackeprang PH","Gaio R","Cihoric N","Zwahlen D","Riggenbach E","Chiang L","Peeken J","Riesterer O","Mose L","Filchenko I","Putz F"],"additional_accession":[]},"is_claimable":false,"name":"Comparative Evaluation of a Medical Large Language Model in Answering Real-World Radiation Oncology Questions: Multicenter Observational Study.","description":"<h4>Background</h4>Large language models (LLMs) hold promise for supporting clinical tasks, particularly in data-driven and technical disciplines such as radiation oncology. While prior evaluation studies have focused on examination-style settings for evaluating LLMs, their performance in real-life clinical scenarios remains unclear. In the future, LLMs might be used as general AI assistants to answer questions arising in clinical practice. It is unclear how well a modern LLM, locally executed within the infrastructure of a hospital, would answer such questions compared with clinical experts.<h4>Objective</h4>This study aimed to assess the performance of a locally deployed, state-of-the-art medical LLM in answering real-world clinical questions in radiation oncology compared with clinical ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-06-04T09:01:27.27Z","creation":"2026-06-01T03:06:36.608Z"},"accession":"S-EPMC12504895","cross_references":{"pubmed":["40986858"],"doi":["10.2196/69752"]}}