ResidencyRL: Reinforcement Learning in Simulated Clinical Environments· ResidencyRL: 模拟临床环境中的强化学习
In medical education, physicians convert academic knowledge into clinical expertise through residency: years of training across thousands of encounters, with diverse sources of feedback and progressively greater autonomy. Much of clinical reasoning relies on the patient encounter, a dialogue in which a clinician elicits history, refines diagnostic hypotheses, and decides management under uncertainty. While large language models (LLMs) excel on static medical benchmarks, methods to optimize the full sequence of clinical decisions remain underdeveloped. We present ResidencyRL, a reinforcement learning (RL) method for training clinical artificial intelligence (AI) agents through simulated multi-turn clinical encounters (up to 60 dialogue turns and 8 tool calls per trajectory). ResidencyRL pai
通过模拟临床环境的多轮对话训练强化学习的临床AI代理。
- 核心方法
- ResidencyRL 方法使用强化学习在模拟的临床环境中训练AI代理，模拟真实的多轮患者对话，包括病史收集、诊断假设修正和不确定条件下的管理决策。
- 适合谁读
- 研究者，工程师，医疗行业专业人士
- 要解决的问题
- 目前大型语言模型在静态医学基准上表现良好，但在模拟临床决策的全流程优化方面尚显不足。
- 关键实验
- 论文报告了在模拟临床环境中进行的实验，展示了ResidencyRL相比其他方法在临床推理任务上的优越性。
- 主要贡献
- 提出了ResidencyRL，一个能够在模拟临床环境中训练AI代理的强化学习模型，通过复杂的多轮对话和工具调用提高临床决策能力。
- 意义与局限
- ResidencyRL 为开发更先进的临床AI系统提供了一种新方法，有助于提高医疗教育质量和医生决策能力。然而，该方法目前仅在模拟环境中测试，实际临床应用还需进一步验证。