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Large language models for structured reporting in radiology: past, present, and future.

Felix Busch1, Lena Hoffmann2, Daniel Pinto Dos Santos3,4

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Summary

Large language models (LLMs) show promise for automating structured reporting (SR) in radiology, despite limited current research. These AI tools could enhance efficiency and accuracy, facilitating wider SR adoption.

Keywords:
Artificial intelligenceElectronic data processingMedical informaticsNatural language processingRadiology

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Area of Science:

  • Artificial Intelligence in Radiology
  • Natural Language Processing for Medical Reports
  • Radiology Information Systems

Background:

  • Structured reporting (SR) aims to standardize radiology reports for improved quality and error reduction.
  • Widespread adoption of SR has been hindered by various challenges.
  • Large language models (LLMs) offer a potential technological solution for automating and facilitating SR.

Purpose of the Study:

  • To provide a narrative review of current literature on LLMs for SR in radiology and related fields.
  • To explore the capabilities and limitations of LLMs in radiology report processing.

Main Methods:

  • Review of existing studies focusing on LLMs (GPT-3.5, GPT-4, Perplexity, Bing Chat, IT5) for SR.
  • Analysis of reported results regarding LLM performance in SR tasks.
  • Identification of key themes including multilingual capabilities, limitations, and regulatory considerations.

Main Results:

  • The current body of literature on LLMs for SR is limited but demonstrates promising outcomes.
  • Several studies utilized generative pre-trained transformer (GPT) models, showing positive results.
  • Multilingual applications of LLMs for SR were found to be feasible in multiple studies.

Conclusions:

  • LLMs possess significant potential to enhance efficiency and accuracy in SR and overall radiology report processing.
  • Overcoming limitations such as opaque algorithms and data concerns is crucial for clinical integration.
  • The future role of LLMs in radiology hinges on addressing regulatory challenges and further research.