Self-Evolving Diagnostic Framework based Gated Residual Adapters and OpenClaw-Based Medical Agents

This Accurate and interpretable report generation from fundus images remains a critical yet challenging task in medical artificial intelligence, particularly due to the static nature of model adaptation and the limited evolvability of existing agent-based frameworks. Although ophthalmic foundation models have significantly improved visual representation learning through large-scale self-supervised pretraining, they lack mechanisms for continual adaptation during inference. Meanwhile, current agent-based approaches enhance reasoning but remain constrained by fixed cognitive structures. In this work, we propose a self-evolving diagnostic framework that unifies parametric adaptation and cognitive evolution for fundus report generation. Specifically, we introduce a gated residual adapter to enable dynamic, inference-time knowledge integration while preserving prior knowledge. Furthermore, we develop a medical agent architecture based on the OpenClaw paradigm, which continuously refines its core reasoning strategy through physician feedback and consistency-driven constraints. By coupling model-level adaptability with agent-level reasoning evolution, the proposed framework enables sustained performance improvement in complex clinical scenarios. This work provides a new perspective on building continuously evolving intelligent diagnostic systems for real-world healthcare applications.