AI and machine learning in medical imaging: key points from development to translation

Area of Science:

• Medical Imaging
• Artificial Intelligence (AI)
• Machine Learning (ML)

Background:

• AI/ML innovation in medical imaging necessitates extensive data, advanced algorithms, and thorough performance evaluations.
• Key assessment areas include generalizability, uncertainty, bias, fairness, trustworthiness, and interpretability.

Purpose of the Study:

• To address critical hurdles in the development and adoption of AI/ML technologies within medical imaging.
• To explore opportunities for advancing AI in radiology by tackling complex clinical translation challenges.

Main Methods:

• Commentary addressing multifaceted challenges in AI/ML model design, development, and performance assessment.
• Discussion of stakeholder engagement, cost-effectiveness, regulatory compliance, and real-world performance feedback loops.

Main Results:

• Widespread integration of AI/ML into clinical tasks faces significant challenges in model design and performance assessment.
• Addressing subtle but critical factors is essential for overcoming adoption barriers.

Conclusions:

• A steadfast commitment to overcoming issues in AI/ML development and performance assessment is vital for clinical integration.
• Comprehensive attention to these factors will drive novel opportunities and advancements in AI-driven radiology.