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<meta name="DC.Title" content="A Review of The Opportunities and Challenges with Large Language Models in Radiology: The Road Ahead" />
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<meta name="DC.Description" content="In recent years, generative artificial intelligence (AI), particularly large language models (LLMs) and their multimodal counterparts, Multi-Modal Large Language Models (MM-LLMs), including Vision Language Models (VLMs), have generated considerable interest in the global AI discourse. LLMs, or pre-trained language models (such as ChatGPT, Med-PaLM, LLaMA, etc.), are neural network architectures trained on extensive text data, excelling in language comprehension and generation. MM-LLMs, a subset of foundation models, are trained on multimodal datasets, integrating text with another modality, such as images, to better learn universal representations akin to human cognition. This versatility enables them to excel in tasks like chatbots, translation, and creative writing while facilitating knowledge sharing through transfer learning, federated learning, and synthetic data creation.

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ABBREVIATIONS: AI: Artificial Intelligence; BERT: Bidirectional Encoder Representations from Transformers; CLIP: Contrastive Language-Image Pretraining; FM: Foundation Models; GPT: Generative Pre-trained Transformer; LLM: Large language model; NLP: natural language processing; VLM: Vision Language Models" />
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<meta name="DC.Contributor" content="Manish Ora" />
<meta name="DC.Contributor" content="Amit Agarwal" />
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&lt;p&gt;In recent years, generative artificial intelligence (AI), particularly large language models (LLMs) and their multimodal counterparts, Multi-Modal Large Language Models (MM-LLMs), including Vision Language Models (VLMs), have generated considerable interest in the global AI discourse. LLMs, or pre-trained language models (such as ChatGPT, Med-PaLM, LLaMA, etc.), are neural network architectures trained on extensive text data, excelling in language comprehension and generation. MM-LLMs, a subset of foundation models, are trained on multimodal datasets, integrating text with another modality, such as images, to better learn universal representations akin to human cognition. This versatility enables them to excel in tasks like chatbots, translation, and creative writing while facilitating knowledge sharing through transfer learning, federated learning, and synthetic data creation.&lt;/p&gt;&lt;p&gt;Several of these models can have potentially appealing applications in the medical domain, including, but not limited to, enhancing patient care by processing patient data, summarizing reports and relevant literature, providing diagnostic, treatment, and follow-up recommendations, and ancillary tasks like coding and billing. As radiologists enter this promising but uncharted territory, it is imperative for them to be familiar with the basic terminology and processes of LLMs. Herein, we present an overview of the LLMs and their potential applications and challenges in the imaging domain.&lt;/p&gt;&lt;p&gt;ABBREVIATIONS: AI: Artificial Intelligence; BERT: Bidirectional Encoder Representations from Transformers; CLIP: Contrastive Language-Image Pretraining; FM: Foundation Models; GPT: Generative Pre-trained Transformer; LLM: Large language model; NLP: natural language processing; VLM: Vision Language Models&lt;/p&gt;" />
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<meta name="twitter:description" content="In recent years, generative artificial intelligence (AI), particularly large language models (LLMs) and their multimodal counterparts, Multi-Modal Large Language Models (MM-LLMs), including Vision Language Models (VLMs), have generated considerable interest in the global AI discourse. LLMs, or pre-trained language models (such as ChatGPT, Med-PaLM, LLaMA, etc.), are neural network architectures trained on extensive text data, excelling in language comprehension and generation. MM-LLMs, a subset of foundation models, are trained on multimodal datasets, integrating text with another modality, such as images, to better learn universal representations akin to human cognition. This versatility enables them to excel in tasks like chatbots, translation, and creative writing while facilitating knowledge sharing through transfer learning, federated learning, and synthetic data creation.

Several of these models can have potentially appealing applications in the medical domain, including, but not limited to, enhancing patient care by processing patient data, summarizing reports and relevant literature, providing diagnostic, treatment, and follow-up recommendations, and ancillary tasks like coding and billing. As radiologists enter this promising but uncharted territory, it is imperative for them to be familiar with the basic terminology and processes of LLMs. Herein, we present an overview of the LLMs and their potential applications and challenges in the imaging domain.

ABBREVIATIONS: AI: Artificial Intelligence; BERT: Bidirectional Encoder Representations from Transformers; CLIP: Contrastive Language-Image Pretraining; FM: Foundation Models; GPT: Generative Pre-trained Transformer; LLM: Large language model; NLP: natural language processing; VLM: Vision Language Models" />
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<meta name="og-description" property="og:description" content="In recent years, generative artificial intelligence (AI), particularly large language models (LLMs) and their multimodal counterparts, Multi-Modal Large Language Models (MM-LLMs), including Vision Language Models (VLMs), have generated considerable interest in the global AI discourse. LLMs, or pre-trained language models (such as ChatGPT, Med-PaLM, LLaMA, etc.), are neural network architectures trained on extensive text data, excelling in language comprehension and generation. MM-LLMs, a subset of foundation models, are trained on multimodal datasets, integrating text with another modality, such as images, to better learn universal representations akin to human cognition. This versatility enables them to excel in tasks like chatbots, translation, and creative writing while facilitating knowledge sharing through transfer learning, federated learning, and synthetic data creation.

Several of these models can have potentially appealing applications in the medical domain, including, but not limited to, enhancing patient care by processing patient data, summarizing reports and relevant literature, providing diagnostic, treatment, and follow-up recommendations, and ancillary tasks like coding and billing. As radiologists enter this promising but uncharted territory, it is imperative for them to be familiar with the basic terminology and processes of LLMs. Herein, we present an overview of the LLMs and their potential applications and challenges in the imaging domain.

ABBREVIATIONS: AI: Artificial Intelligence; BERT: Bidirectional Encoder Representations from Transformers; CLIP: Contrastive Language-Image Pretraining; FM: Foundation Models; GPT: Generative Pre-trained Transformer; LLM: Large language model; NLP: natural language processing; VLM: Vision Language Models" />
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      <h1 class="highwire-cite-title" id="page-title">A Review of The Opportunities and Challenges with Large Language Models in Radiology: The Road Ahead</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">Neetu Soni</span>, <span class="highwire-citation-author" data-delta="1">Manish Ora</span>, <span class="highwire-citation-author" data-delta="2">Amit Agarwal</span>, <span class="highwire-citation-author" data-delta="3">Tianbao Yang</span> and <span class="highwire-citation-author" data-delta="4">Girish Bathla</span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">November 2024,  </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">ajnr.A8589; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A8589 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-111972--61800634985" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Neetu Soni </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'>Department of Radiology, Mayo Clinic, Jacksonville, 4500 San Pablo Road, Jacksonville, FL 55902, USA (N.S, A.A), Department of Nuclear Medicine, Sanjay Gandhi post graduate institute of medical science, Lucknow 226014, India, (MO), Department of Computer Science & Engineering Texas A&M University (T.Y), Department of Radiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55902, USA (G.B)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Neetu%2BSoni%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li 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USA (G.B)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Manish%2BOra%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Ora%20M&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AManish%2BOra%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">Amit Agarwal </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'>Department of Radiology, Mayo Clinic, Jacksonville, 4500 San Pablo Road, Jacksonville, FL 55902, USA (N.S, A.A), Department of Nuclear Medicine, Sanjay Gandhi post graduate institute of medical science, Lucknow 226014, India, (MO), Department of Computer Science & Engineering Texas A&M University (T.Y), Department of Radiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55902, USA (G.B)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Amit%2BAgarwal%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Agarwal%20A&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AAmit%2BAgarwal%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">Tianbao Yang </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'>Department of Radiology, Mayo Clinic, Jacksonville, 4500 San Pablo Road, Jacksonville, FL 55902, USA (N.S, A.A), Department of Nuclear Medicine, Sanjay Gandhi post graduate institute of medical science, Lucknow 226014, India, (MO), Department of Computer Science & Engineering Texas A&M University (T.Y), Department of Radiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55902, USA (G.B)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Tianbao%2BYang%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Yang%20T&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3ATianbao%2BYang%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">Girish Bathla </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'>Department of Radiology, Mayo Clinic, Jacksonville, 4500 San Pablo Road, Jacksonville, FL 55902, USA (N.S, A.A), Department of Nuclear Medicine, Sanjay Gandhi post graduate institute of medical science, Lucknow 226014, India, (MO), Department of Computer Science & Engineering Texas A&M University (T.Y), Department of Radiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55902, USA (G.B)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Girish%2BBathla%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Bathla%20G&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AGirish%2BBathla%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div></div></div>  </div>

  
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