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Large language models in radiology: fundamentals, applications, ethical considerations, risks, and future directions.

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Title: Large language models in radiology: fundamentals, applications, ethical considerations, risks, and future directions.
Authors: D'Antonoli, Tugba Akinci, Stanzione, Arnaldo, Bluethgen, Christian, Vernuccio, Federica, Ugga, Lorenzo, Klontzas, Michail E., Cuocolo, Renato, Cannella, Roberto, Koçak, Burak
Source: Diagnostic & Interventional Radiology; Mar2024, Vol. 30 Issue 2, p80-90, 11p
Subject Terms: LANGUAGE models, ARTIFICIAL intelligence in medicine, RADIOLOGISTS, CHATGPT, NATURAL language processing
Abstract: With the advent of large language models (LLMs), the artificial intelligence revolution in medicine and radiology is now more tangible than ever. Every day, an increasingly large number of articles are published that utilize LLMs in radiology. To adopt and safely implement this new technology in the field, radiologists should be familiar with its key concepts, understand at least the technical basics, and be aware of the potential risks and ethical considerations that come with it. In this review article, the authors provide an overview of the LLMs that might be relevant to the radiology community and include a brief discussion of their short history, technical basics, ChatGPT, prompt engineering, potential applications in medicine and radiology, advantages, disadvantages and risks, ethical and regulatory considerations, and future directions. [ABSTRACT FROM AUTHOR]
Copyright of Diagnostic & Interventional Radiology is the property of Galenos Yayinevi Tic. LTD. STI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Large language models in radiology: fundamentals, applications, ethical considerations, risks, and future directions.
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  Data: Diagnostic & Interventional Radiology; Mar2024, Vol. 30 Issue 2, p80-90, 11p
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  Data: <searchLink fieldCode="DE" term="%22LANGUAGE+models%22">LANGUAGE models</searchLink><br /><searchLink fieldCode="DE" term="%22ARTIFICIAL+intelligence+in+medicine%22">ARTIFICIAL intelligence in medicine</searchLink><br /><searchLink fieldCode="DE" term="%22RADIOLOGISTS%22">RADIOLOGISTS</searchLink><br /><searchLink fieldCode="DE" term="%22CHATGPT%22">CHATGPT</searchLink><br /><searchLink fieldCode="DE" term="%22NATURAL+language+processing%22">NATURAL language processing</searchLink>
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  Data: With the advent of large language models (LLMs), the artificial intelligence revolution in medicine and radiology is now more tangible than ever. Every day, an increasingly large number of articles are published that utilize LLMs in radiology. To adopt and safely implement this new technology in the field, radiologists should be familiar with its key concepts, understand at least the technical basics, and be aware of the potential risks and ethical considerations that come with it. In this review article, the authors provide an overview of the LLMs that might be relevant to the radiology community and include a brief discussion of their short history, technical basics, ChatGPT, prompt engineering, potential applications in medicine and radiology, advantages, disadvantages and risks, ethical and regulatory considerations, and future directions. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Diagnostic & Interventional Radiology is the property of Galenos Yayinevi Tic. LTD. STI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.4274/dir.2023.232417
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              Text: Mar2024
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