Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice.

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.