Understanding Continual Learning for Medical Imaging: Challenges and Trend

What is it about?

Artificial intelligence systems used in medical imaging are often trained once and then deployed in real hospitals. However, medical data changes over time: new scanners are introduced, imaging protocols evolve, and patient populations differ across locations. When AI models are retrained on new data, they often “forget” what they learned before, which can reduce reliability and safety. Continual learning is a family of methods designed to help AI systems learn from new data over time without losing previously acquired knowledge. This paper provides a comprehensive survey of continual learning methods specifically in the context of medical imaging. We review how these methods have been applied to tasks such as disease detection, segmentation, and classification across different imaging modalities. The paper explains the main challenges faced when applying continual learning in healthcare, including data privacy constraints, limited annotations, and differences between hospitals and devices. In addition to reviewing existing approaches, we analyze how continual learning methods are evaluated in medical imaging research and highlight common limitations in current experimental practices. We discuss which techniques work well in practice, where they fail, and why results can be difficult to compare across studies. The paper also outlines practical recommendations for researchers and practitioners who want to apply continual learning in real clinical settings. Overall, this work aims to help bridge the gap between continual learning research and real-world medical imaging applications by clarifying current progress, identifying open challenges, and suggesting directions for future research.

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Photo by Markus Winkler on Unsplash

Photo by Markus Winkler on Unsplash

Why is it important?

Medical imaging AI systems are increasingly being used in real clinical environments, but most current models are trained in static settings and struggle to adapt when data changes over time. This creates a gap between research prototypes and systems that can be safely deployed in hospitals. Continual learning offers a promising solution, but its use in medical imaging is still fragmented and difficult to evaluate consistently. This work is important because it brings together, for the first time, a structured and critical overview of continual learning methods specifically tailored to medical imaging. Unlike prior surveys that focus on general machine learning, this paper highlights domain-specific challenges such as data privacy, limited access to historical data, and clinical reliability requirements. By analyzing how existing methods perform under realistic medical scenarios, the paper clarifies which approaches are practical and where current techniques fall short. The paper is also timely, as healthcare institutions are increasingly interested in deploying adaptive AI systems that can remain reliable over long periods without frequent full retraining. By examining evaluation practices and common pitfalls in current research, this work helps improve reproducibility and comparability across studies. It provides practical guidance that can inform future research, benchmark design, and clinical translation. Overall, this work helps researchers, clinicians, and developers better understand how continual learning can be responsibly applied in medical imaging, ultimately supporting the development of AI systems that are more robust, trustworthy, and suitable for real-world healthcare use.

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