Physician-in-the-Loop Active Learning in Radiology Artificial Intelligence Workflows: Opportunities, Challenges, and...

Area of Science:

• Radiology
• Artificial Intelligence
• Machine Learning

Background:

• Artificial intelligence (AI) applications in radiology are expanding, including image reconstruction, segmentation, classification, and workflow optimization.
• Training accurate AI models necessitates large, expert-labeled datasets, which are costly and time-consuming to acquire.
• Active learning presents a solution to mitigate labeling requirements in data-limited scenarios.

Purpose of the Study:

• To explore the application of active learning in radiology AI.
• To highlight active learning's role in reducing resource needs for training radiology AI models.
• To enhance physician-AI interaction and collaboration within radiology workflows.

Main Methods:

• Review of literature on active learning strategies in the context of radiology AI.
• Discussion of active learning concepts and their application to radiology tasks.
• Presentation of use cases and literature-based examples.

Main Results:

• Active learning identifies the most informative data for human annotation, reducing the overall labeling burden.
• This targeted annotation improves AI model performance, especially with constrained datasets.
• Active learning facilitates the development of physician-in-the-loop AI systems.

Conclusions:

• Active learning is a valuable strategy for efficient radiology AI development.
• Integration of active learning can optimize resource allocation and enhance physician-AI collaboration.
• Further research and implementation are recommended to address challenges and capitalize on opportunities.