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[Artificial intelligence for diagnostic imaging].

Claes Nøhr Ladefoged1, Flemming Littrup Andersen, Liselotte Højgaard

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Summary
This summary is machine-generated.

Artificial intelligence (AI) in diagnostic imaging offers benefits like improved patient flow and reduced radiation dose. Evidence-based AI algorithms, rigorously tested, should be implemented if they outperform current methods.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) systems offer potential improvements in diagnostic imaging.
  • Applications include enhancing patient flow, optimizing image processing, reducing scan times, and lowering radiation doses.
  • AI can serve as a decision support tool for image interpretation.

Purpose of the Study:

  • To review and propose a framework for the evidence-based development and implementation of AI in diagnostic imaging.
  • To outline the necessary steps for validating AI algorithms in a clinical setting.

Main Methods:

  • The review advocates for a structured approach to AI development.
  • This includes hypothesis generation, algorithm selection and training on initial datasets.
  • Subsequent testing on new datasets and prospective clinical studies are crucial.

Main Results:

  • The review emphasizes the need for rigorous, evidence-based validation of AI algorithms.
  • Successful AI implementation requires demonstrating superior or cost-effective performance compared to existing methodologies.

Conclusions:

  • AI algorithms in diagnostic imaging must be developed and validated using a robust, evidence-based framework.
  • Implementation should be contingent on AI demonstrating clear advantages in efficacy or efficiency over conventional methods.