Physician-in-the-Loop Active Learning in Radiology Artificial Intelligence Workflows: Opportunities, Challenges, and Future Directions.
Authors
Affiliations (8)
Affiliations (8)
- Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada.
- Department of Radiology, Mayo Clinic, Radiology Informatics Laboratory, Rochester, MN, USA.
- School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.
- Department of Molecular Imaging and Therapy, BC Cancer, Vancouver, BC, Canada.
- BC Children's Hospital, Vancouver, BC, Canada.
- Department of Radiology and Molecular Imaging, College of Medicine and Health Science, Sultan Qaboos University, Oman.
- Institute of Nuclear Medicine, Bethesda, MD, USA.
Abstract
Artificial intelligence (AI) is being explored for a growing range of applications in radiology, including image reconstruction, image segmentation, synthetic image generation, disease classification, worklist triage, and examination scheduling. However, training accurate AI models typically requires substantial amounts of expert-labeled data, which can be time-consuming and expensive to obtain. Active learning offers a potential strategy for mitigating the impacts of such labeling requirements. In contrast with other machine-learning approaches used for data-limited situations, active learning aims to produce labeled datasets by identifying the most informative or uncertain data for human annotation, thereby reducing labeling burden to improve model performance under constrained datasets. This Review explores the application of active learning to radiology AI, focusing on the role of active learning in reducing the resources needed to train radiology AI models while enhancing physician-AI interaction and collaboration. We discuss how active learning can be incorporated into radiology workflows to promote physician-in-the-loop AI systems, presenting key active learning concepts and use cases for radiology-based tasks, including through literature-based examples. Finally, we provide summary recommendations for the integration of active learning in radiology workflows while highlighting relevant opportunities, challenges, and future directions.