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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
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The radiologist and data: Do we add value or is data just data?

Elliot K Fishman1, Philippe Soyer2, David B Hellmann3

  • 1The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Baltimore, MD 21287, United States of America.

Clinical Imaging
|April 19, 2025
PubMed
Summary
This summary is machine-generated.

High-quality data is essential for artificial intelligence in radiology. Physician involvement in data curation and interpretation is vital for developing clinically relevant AI models, treating data as a public good.

Keywords:
Artificial intelligenceDataTeam science

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Data Science

Background:

  • Artificial intelligence (AI) in radiology requires substantial high-quality datasets.
  • Data ownership and secondary use of clinical data are subjects of ongoing debate.

Purpose of the Study:

  • To propose a framework for the secondary use of clinical data in radiology AI development.
  • To emphasize the value of physician input in creating clinically relevant AI models.

Main Methods:

  • Conceptual framework development.
  • Literature review on data ownership and AI in radiology.
  • Argumentation for data as a public good.

Main Results:

  • Clinical data, when used secondarily, should be viewed as a public good.
  • Physician expertise significantly enhances the value of data for AI.
  • Clinically relevant AI models depend on curated and interpreted data.

Conclusions:

  • Physician input is indispensable for developing effective AI in radiology.
  • Treating clinical data as a public good facilitates AI advancement.
  • Addressing data ownership is key to unlocking AI's potential in healthcare.