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Summary

Machine learning models for radiology image interpretation face challenges in clinical adoption. This paper outlines key considerations for developing accurate, robust, and practical AI tools in medical imaging.

Keywords:
AIartificial intelligencedeep learningdeploymentmachine learningneural networksprivacyradiology

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis
  • Radiology Informatics

Background:

  • Machine learning (ML) shows promise for intelligent radiology image interpretation.
  • Clinical adoption of ML models is hindered by numerous practical challenges.
  • Addressing these challenges is crucial for reliable AI in healthcare.

Purpose of the Study:

  • To identify and compile critical considerations for developing practical ML models in medical imaging.
  • To provide a comprehensive checklist for researchers and stakeholders.
  • To enhance the accuracy, robustness, and usability of AI tools in clinical radiology.

Main Methods:

  • Systematic review and synthesis of challenges in ML for medical imaging.
  • Categorization of key considerations including data, model performance, and ethical aspects.
  • Identification of techniques to address each identified challenge.

Main Results:

  • Key considerations include data limitations (insufficient data, decentralization, annotation costs, ground truth ambiguity, class imbalance), model performance issues (generalization, decay, adversarial attacks), and ethical concerns (explainability, fairness, bias).
  • Specific techniques are presented for each challenge to improve model development and validation.
  • The compilation serves as a practical guide for ML researchers and clinical practitioners.

Conclusions:

  • Overcoming challenges in data, model robustness, and ethical deployment is essential for successful ML adoption in radiology.
  • A structured approach to these considerations can accelerate the development of trustworthy AI tools.
  • This work aims to bridge the gap between ML research and clinical application in medical imaging.