AI医生来了：一个模型搞定问诊、读片、写病历
医疗AI通常只擅长一件事：要么会聊天，要么会读片，要么会写病历。但真实医生得同时干这三样。这篇论文搞了个叫Cura 1T的模型，用一套「自我进化」机制——每次训练后自动找出哪里翻车，再针对性地补数据重练，而不是一股脑塞更多医学资料。结果它在问诊、读片、诊断、操作电子病历等所有任务上都排进前三，而且没牺牲通用能力。它不是你明天就能挂号的AI医生，但它是第一个证明「一个模型能覆盖医生全部工作」的里程碑。
📄 原文摘要(英文)
Healthcare spans high-stakes communication, expert reasoning, and workflow execution, yet specialized LLMs that cover these use cases together remain limited. A healthcare model must handle patient consultation, clinical reasoning over text and images, interactive diagnosis, and electronic health record (EHR) tool use. These capabilities fail in different ways, and a narrow update for one task can degrade another. We present Cura 1T, a healthcare-specialized LLM trained through a human-gated self-evolution loop. In each evolution round, a training agent plans a target capability, trains the model, evaluates benchmark trajectories, and refines the data mixture from observed failures. This data-centered loop improves the model through targeted synthetic and curated examples rather than a single generic medical-data update. Across the healthcare evaluation suite, Cura 1T ranks at or near the top among frontier baselines, while remaining competitive on out-of-domain reasoning and agentic benchmarks.