Enterprise AI Analysis
Continual Learning in Medical Imaging: A Survey and Practical Analysis
Authored by MOHAMMAD AREEB QAZI, ANEES UR HASHMI, SANTOSH SANJEEV, IBRAHIM ALMAKKY, NUMAN SAEED, CAMILA GONZÁLEZ, MOHAMMAD YAQUB and published on February 2026, this paper surveys the critical advancements and challenges in applying continual learning (CL) to medical imaging. It highlights the necessity for AI models to adapt to evolving clinical data without 'catastrophic forgetting', ensuring long-term reliability and performance in real-world healthcare settings. The work also emphasizes the need for standardization, reproducibility, and regulatory compliance for practical deployment.
Executive Impact & Key Findings
This research underscores the potential of Continual Learning to revolutionize medical AI, allowing systems to evolve and improve over time, critical for dynamic clinical environments.
4
Total Citations
111
Total Downloads
1
AvgACC (Average Accuracy)
1
FAA (Final Average Accuracy)
0.1
BWT (Backward Transfer)
0.1
AvgF (Average Forgetting)
Deep Analysis & Enterprise Applications
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Background & Formalization
The paper introduces the fundamental concepts of Continual Learning (CL) in medical imaging, outlining its mathematical formulation and three primary types: Task Incremental Learning (TIL), Domain Incremental Learning (DIL), and Class Incremental Learning (CIL). It details the objective functions for each type, aiming to optimize model parameters to learn new tasks/domains/classes while retaining previous knowledge through regularization terms. This section also covers key evaluation metrics like Average Accuracy (AvgACC), Final Average Accuracy (FAA), Backward Transfer (BWT), and Average Forgetting (AvgF) to assess performance across sequential tasks.
Regularization Approaches
This category includes methods that prevent catastrophic forgetting by minimizing drift in learned feature or parameter spaces, often by adding loss terms or maintaining a copy of previous models. These are lightweight and privacy-preserving, suitable for medical applications due to limited data access. Subcategories include Parameter Regularization (e.g., EWC, LWF, MAS) which penalizes changes in important network parameters, and Functional Regularization (e.g., distillation-based) which keeps the learned feature space consistent between tasks without explicit parameter constraints. Challenges include performance deterioration over time and balancing plasticity with stability.
Replay-based Approaches
Replay-based methods utilize a memory buffer to store and reintroduce prior experiences (samples, input-output pairs, or representations) when learning new tasks. This approach is highly effective in retaining critical patterns and diagnoses, crucial for medical applications. Techniques range from pseudo-rehearsal, generative replay (using GANs/VAEs to create synthetic data to mitigate privacy concerns), and dynamic memory modules that adapt to data shifts. While powerful, they introduce challenges related to data privacy, model interpretability, and regulatory validation, which need careful mitigation.
Dynamic Model Approaches
These methods tackle catastrophic forgetting by intelligently introducing new parameters or expanding the model structure for each new task, while maintaining shared features. Examples include using domain-specific batch normalization layers, task-agnostic network expansion based on distance metrics (e.g., Mahalanobis distance), and class-specific heads for multi-organ segmentation (leveraging CLIP embeddings). The goal is to allow effective learning of task-specific features without re-training the entire model. Challenges include identifying the correct task ID during inference and the computational cost/practicality of extensive training.
42.7%
of surveyed methods are Regularization-based
Continual Learning in Medical Imaging: Core Approaches
Regularization (Parameter & Functional)
→
Replay (Data & Feature)
→
Dynamic (Expanding & Fixed)
| Approach | Key Advantages | Main Challenges |
|---|---|---|
| Regularization-based |
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| Replay-based |
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| Dynamic Model-based |
|
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Real-world Impact: Diabetic Retinopathy Detection
The Google Health diabetic retinopathy detection system, deployed in 2020, demonstrated promising static performance. However, upon real-world deployment, its performance significantly deteriorated due to distribution shifts in the continuous data stream. This case highlights the critical need for Continual Learning (CL) to enable AI models to adapt to evolving clinical data without catastrophic forgetting, ensuring sustained effectiveness and reliability in medical diagnostics. Retraining on entire datasets is impractical due to computational costs and privacy concerns, making CL an indispensable solution.
Calculate Your Potential ROI with CL in Healthcare
Estimate the efficiency gains and cost savings your organization could achieve by implementing Continual Learning for your AI systems.
Annual Savings
$0
Hours Reclaimed Annually
0
Your Path to Continual Learning in Medical AI
Based on our analysis, here’s a strategic roadmap for integrating Continual Learning into your enterprise, tailored to the unique demands of healthcare.
Specialized CL Datasets in the Medical Domain
Develop datasets tailored to unique CL demands in medical imaging to advance methodologies within this specialized field.
Accessible Code Repositories for CL Methodologies
Foster collaboration and accelerate research by providing readily available and replicable codebases.
Emphasizing Explainability and Interpretability in CL Models
Prioritize transparency and interpretability of CL models for stakeholder trust and clinical integration.
Practical Deployment Strategies for CL Models in Healthcare
Devise deployment protocols aligning with regulatory frameworks and scalability for real-world adoption.
Conclusion: Adapting AI for Future Healthcare
The increasing interest in Continual Learning (CL) within medical imaging is driven by the need for AI models to adapt to continuous data streams and distribution shifts without catastrophic forgetting. This survey highlights significant progress in regularization-based, replay-based, and dynamic model-based approaches. Future work must address critical issues such as standardization, task boundary handling, reproducibility, and regulatory compliance. The field is poised for further advancements, particularly in developing task-agnostic, explainable, and deployable solutions to unlock the full potential of CL in healthcare.
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