Clinical expertise improves not only by acquiring medical knowledge, but by accumulating experience that yields reusable diagnostic patterns. Recent LLMs-based diagnostic agents have shown promising progress in clinical reasoning for decision support. However, most approaches treat cases independently, limiting experience reuse and continual adaptation. We propose SEA, a self-learning diagnostic agent with cognitively inspired dual-memory module. We design a reinforcement training framework tailored to our designed agent for joint optimization of reasoning and memory management. We evaluate SEA in two complementary settings. On standard evaluation with MedCaseReasoning dataset, SEA achieves 92.46% accuracy, outperforming the strongest baseline by +19.6%, demonstrating the benefit of jointly optimizing reasoning and memory. On the long-horizon with ER-Reason dataset, SEA attains the best final accuracy (0.7214) and the largest improvement (+0.35 Acc@100), while baseline methods show limited or unstable gains. Expert evaluation further indicates that rules consolidated from SEA show strong clinical correctness, usefulness and trust, suggesting that the induced rules in dual-memory module are reliable and practically meaningful. Overall, SEA improves both diagnostic reasoning ability and continual learning by effectively transforming experience into reusable knowledge.

翻译：临床专业能力的提升不仅依赖于医学知识的获取，更源于积累可复用的诊断模式的经验积累。当前基于大语言模型的诊断智能体在临床决策支持的推理方面已展现出显著进展，但多数方法将病例孤立处理，限制了经验复用与持续适应能力。我们提出SEA——一种具有认知启发式双记忆模块的自学习诊断智能体，并设计了一套专为该智能体定制的强化训练框架，以实现推理过程与记忆管理的联合优化。我们在两个互补场景中评估SEA：在MedCaseReasoning数据集的标准评估中，SEA达到92.46%的准确率，以+19.6%的优势超越最强基线方法，证明联合优化推理与记忆的有效性；在ER-Reason数据集的长期演化评估中，SEA获得最佳最终准确率（0.7214）与最大提升幅度（+0.35 Acc@100），而基线方法呈现有限或不稳定的增益。专家评估进一步表明，SEA提炼的规则在临床正确性、实用性与可信度方面表现优异，这意味着双记忆模块中的归纳规则具有可靠性与实际应用价值。总体而言，SEA通过将经验有效转化为可复用知识，同时提升了诊断推理能力与持续学习性能。