Unleashing the power of data in radiology: A look ahead

Peering into the future of radiology, we find ourselves at an exciting crossroads. The past year has seen language-based foundation models disrupt the status quo, offering a tantalising glimpse into new possibilities for data accessibility. Yet we still face a huge challenge: a wealth of valuable data remains locked away in free-text reports. But what if we could unlock this treasure chest? Imagine embedding this new type of AI into innovative tools to transform these reports into actionable data.

However, this transformation is not just about technology. It’s about changing mindsets within our hospitals to build pipelines that channel medical data into structured databases. The process could involve using standardised templates for primary data acquisition, or the application of tailored natural language processing (NLP) tools for efficient data mining afterwards – or ideally a mix of both. Depending on the structure and heterogeneity of the reports, a range of NLP methods, from rule-based to AI-based, such as fine-tuned large language models (LLMs), may be required for accurate information retrieval.

The shift to a data-centric healthcare system also depends on cultivating an AI-literate medical workforce; this is especially true for our radiology residents, the ‘digital natives’ at the forefront of this transformation. Despite their digital affinity, they often lack the comprehensive skills to use these tools effectively in clinical and scientific contexts. Therefore, their training should not only cover the pros and cons of AI, but also focus on understanding key concepts and critically evaluating AI models. As AI continues to permeate healthcare, we can expect a surge in clinicians seeking this specialised training. Opportunities already exist, such as the ESR Master Class in AI and the RSNA Imaging AI Certificate Program. As a forward-thinking and fully digitised discipline, radiology is an ideal field in which to learn how to use these new innovative tools and software architectures based on text- and image-based medical data.

Article: From data to insights: how natural language processing and structured reporting advance data-driven radiology

Author: Matthias A. Fink

WRITTEN BY

  • Matthias Fink

    Clinic for Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany; Translational Lung Research Center Heidelberg, Member of the German Center for Lung Research, Heidelberg, Germany

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Footnotes:

01

Reduced registration fees for ECR 2024:
Provided that ESR 2023 membership is activated and approved by August 31, 2023.

Reduced registration fees for ECR 2025:
Provided that ESR 2024 membership is activated and approved by August 31, 2024.

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European Radiology, Insights into Imaging, European Radiology Experimental.