arXiv:2511.20732v1 Announce Type: new
Abstract: Medical AI systems face catastrophic forgetting when deployed in clinical settings, where models must learn new imaging protocols while retaining prior diagnostic capabilities. This challenge is particularly acute for medical vision-language models that must preserve complex cross-modal alignments between medical images and clinical terminology across diverse imaging modalities. We introduce Prompt- Aware Adaptive Elastic Weight Consolidation (PA-EWC), a novel continual learning approach that addresses catastrophic forgetting through prompt-guided parameter specialization. Our method systematically categorizes model parameters based on their functional roles in processing visual-descriptive, spatial-guided, and medical-semantic information, enabling targeted protection of critical knowledge while allowing adaptation to new clinical requirements. PA-EWC incorporates adaptive Fisher Information computation with gradient stability analysis and develops weighted complexity metrics based on medical terminology density. We evaluate our approach across five medical imaging datasets (Kvasir-SEG, ISIC 2018, CheXlocalize, BUSI, CAMUS) representing diverse modalities including endoscopy, dermoscopy, radiography, and ultrasound. Experimental results demonstrate that PA-EWC reduces catastrophic forgetting by up to 17.58% compared to baseline methods, with performance improvements of 4.30% on chest X-ray pathology localization and 6.06% on polyp segmentation.
Expert Commentary:
In this groundbreaking research, the authors address a crucial challenge faced by medical AI systems when deployed in clinical settings: the issue of catastrophic forgetting. This phenomenon occurs when AI models must learn new imaging protocols while retaining their prior diagnostic capabilities. The authors specifically focus on medical vision-language models, which require complex cross-modal alignments between medical images and clinical terminology across various imaging modalities.
The proposed solution, Prompt-Aware Adaptive Elastic Weight Consolidation (PA-EWC), is a novel continual learning approach that leverages prompt-guided parameter specialization to protect critical knowledge while allowing adaptation to new clinical requirements. By categorizing model parameters based on their functional roles, PA-EWC enables targeted retention of important information while facilitating flexibility for learning new tasks.
What sets PA-EWC apart is its incorporation of adaptive Fisher Information computation, gradient stability analysis, and weighted complexity metrics based on medical terminology density. These sophisticated techniques help mitigate catastrophic forgetting and enhance model performance across diverse medical imaging datasets.
This research not only showcases the importance of continual learning in the context of medical AI systems but also highlights the multi-disciplinary nature of the concepts involved. By integrating insights from fields such as computer vision, natural language processing, and medical imaging, the authors have developed a comprehensive approach that has the potential to significantly impact the field of multimedia information systems.
Moreover, the implications of this work extend beyond medical AI to other areas such as animations, artificial reality, augmented reality, and virtual realities. The ability to adapt to new information while preserving existing knowledge is a fundamental challenge in these domains as well, making the novel approach presented in this paper a valuable contribution to the broader landscape of advanced technologies.