{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T09:17:27Z","timestamp":1758273447779,"version":"3.41.2"},"reference-count":15,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T00:00:00Z","timestamp":1723593600000},"content-version":"vor","delay-in-days":246,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,12,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Large language models (LLMs) are transforming the field of natural language processing (NLP). These models offer opportunities for radiologists to make a meaningful impact in their field. NLP is a part of artificial intelligence (AI) that uses computer algorithms to study and understand text data. Recent advances in NLP include the Attention mechanism and the Transformer architecture. Transformer-based LLMs, such as GPT-4 and Gemini, are trained on massive amounts of data and generate human-like text. They are ideal for analysing large text data in academic research and clinical practice in radiology. Despite their promise, LLMs have limitations, including their dependency on the diversity and quality of their training data and the potential for false outputs. Albeit these limitations, the use of LLMs in radiology holds promise and is gaining momentum. By embracing the potential of LLMs, radiologists can gain valuable insights and improve the efficiency of their work. This can ultimately lead to improved patient care.<\/jats:p>","DOI":"10.1093\/bjro\/tzae022","type":"journal-article","created":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T16:52:07Z","timestamp":1723654327000},"source":"Crossref","is-referenced-by-count":2,"title":["Advancing radiology practice and research: harnessing the potential of large language models amidst imperfections"],"prefix":"10.1093","volume":"6","author":[{"given":"Eyal","family":"Klang","sequence":"first","affiliation":[{"name":"Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai , New York, NY, 10029-6504, United States"},{"name":"The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai , New York, NY, 10029-6504, United 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Gan, 52621, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Girish N","family":"Nadkarni","sequence":"additional","affiliation":[{"name":"Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai , New York, NY, 10029-6504, United States"},{"name":"The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai , New York, NY, 10029-6504, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6276-9937","authenticated-orcid":false,"given":"Eyal","family":"Zimlichman","sequence":"additional","affiliation":[{"name":"Tel Aviv University School of Medicine, Tel Aviv University , Tel Aviv, 69978, Israel"},{"name":"The Sheba Talpiot Medical Leadership Program, Sheba Medical Center, Ramat Gan, 52621, Israel"},{"name":"Hospital Management, Sheba Medical Center, Ramat Gan, 52621, 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