Continual Learning Framework for Adaptive Medical Diagnosis Systems

Artificial intelligence systems used in medical diagnosis are typically trained on large medical imaging datasets, but they often struggle to adapt when new diseases or clinical data emerge. Updating these models using traditional training approaches can overwrite previously learned knowledge, leading to a problem known as catastrophic forgetting. This study proposes a continual learning framework for adaptive medical diagnosis systems that enables models to learn new medical conditions while retaining earlier knowledge. The framework integrates techniques such as Elastic Weight Consolidation (EWC) and Experience Replay (ER) to support stable sequential learning. Experiments conducted on chest imaging datasets involving pneumonia, COVID-19, and lung cancer demonstrate that the proposed approach reduces forgetting while maintaining strong diagnostic accuracy. The results highlight the potential of continual learning to build reliable, adaptive, and long-term AI systems for real-world healthcare environments.