Evaluating artificial intelligence for medical imaging: a primer for clinicians

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Machine Learning

Background:

• Artificial intelligence (AI) holds significant potential to transform medical imaging practices.
• Effective clinical integration necessitates a thorough understanding of AI capabilities and limitations.

Purpose of the Study:

• To provide a comprehensive overview of AI in medical imaging, covering key use cases and foundational machine learning concepts.
• To establish a framework for assessing the clinical effectiveness and generalizability of AI studies in medical imaging.
• To discuss barriers to clinical translation and outline future directions for AI in the field.

Main Methods:

• Review of key clinical applications: detection, classification, segmentation, and radiomics.
• Explanation of machine learning fundamentals: learning types, strategies, training, and evaluation.
• Development of a theoretical framework for appraising the internal validity and generalizability of AI studies.

Main Results:

• AI offers diverse applications in medical imaging, from image analysis to predictive modeling.
• Understanding machine learning processes is crucial for effective AI implementation.
• A framework for evaluating AI study validity and generalizability is proposed.

Conclusions:

• Clinicians need awareness of AI capabilities, limitations, and evaluation methods for informed adoption.
• Addressing barriers and exploring future directions like multi-modal data integration and explainability are key for advancing AI in medical imaging.
• Strategic adoption of AI in medical imaging can enhance patient care and clinical outcomes.