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Natural language processing and LLMs in liver imaging: a practical review of clinical applications.

Liver diseases pose a significant global health challenge due to their silent progression and high mortality. Proper interpretation of radiology reports is essential for the evaluation and management of these conditions but is limited by variability in reporting styles and the complexity of unstruct...

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Published in:Abdominal Radiology Vol. 51; no. 3; pp. 1595 - 1608
Main Authors: López-Úbeda, Pilar, Martín-Noguerol, Teodoro, Luna, Antonio
Format: Journal Article
Published: Springer Nature Mar2026
Online Access:View this record in EBSCOhost
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      dt: Mar2026
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      pub: Springer Nature
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        atl: Natural language processing and LLMs in liver imaging: a practical review of clinical applications.
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          López-Úbeda, Pilar
          Martín-Noguerol, Teodoro
          Luna, Antonio
        affil: HT Médica, Jaén, Spain
      sug:
      ab: Liver diseases pose a significant global health challenge due to their silent progression and high mortality. Proper interpretation of radiology reports is essential for the evaluation and management of these conditions but is limited by variability in reporting styles and the complexity of unstructured medical language. In this context, Natural Language Processing (NLP) techniques and Large Language Models (LLMs) have emerged as promising tools to extract relevant clinical information from unstructured liver radiology reports. This work reviews, from a practical point of view, the current state of NLP and LLM applications for liver disease classification, clinical feature extraction, diagnostic support, and staging from reports. It also discusses existing limitations, such as the need for high-quality annotated data, lack of explainability, and challenges in clinical integration. With responsible and validated implementation, these technologies have the potential to transform liver clinical management by enabling faster and more accurate diagnoses and optimizing radiology workflows, ultimately improving patient care in liver diseases.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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