Medical Long Context Reasoning (MLCR-AA)
MLCR-AA Score
MLCR-AA Accuracy (Judged Responses)
MLCR-AA Completeness (Judged Responses)
MLCR-AA Conciseness
得分
MLCR-AA：得分
MLCR-AA：得分 vs. 每项任务成本
Medical Long Context Reasoning（MLCR-AA）已评判回答的准确性与完整性
Token 使用量
MLCR-AA：每项任务的输出 Token
成本
MLCR-AA：每项任务成本
速度
MLCR-AA：每项任务耗时
得分 vs. 发布日期
MLCR-AA：得分 vs. 发布日期
示例任务
常见问题
MLCR-AA 是 Artificial Analysis 对 MLCR 的评测。MLCR 是 Wisedocs 推出的开放基准，用于测试 AI 模型能否对冗长、零散的医疗记录进行推理，并像理赔专业人员审核保险和医疗案例一样完成跨文档综合。案例是约 25,000 至 64,000 个 token 的合成医疗档案，问题分为六个难度等级，从查找单一事实到专家级临床综合和复合多部分推理。
每个模型回答首先必须通过简洁性门槛：长度超过参考答案五倍的回答得零分。随后由三个 LLM 组成的评审组对简洁回答进行评审，通过多数表决判断准确性和完整性。主要的整体通过率只有在回答简洁且被判定为完整、准确时才计分。本页面的主要指标是在专家级和复合多部分（困难）问题集上的整体通过率。
在已发布 MLCR-AA 结果的模型中，Claude Sonnet 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) 得分最高，整体通过率为 75%。 查看模型
理赔专业人员审核涵盖数百次就诊的记录时，不只是查找单一事实，还要在零散文档中重建时间顺序、因果关系、治疗模式和理赔相关性。MLCR-AA 正是衡量这种能力，对评估现成 AI 是否适用于医疗记录审核、保险理赔和其他长文档医疗工作流的组织十分重要。
不是。MLCR-AA 作为独立评测发布，不计入 Artificial Analysis Intelligence Index。Artificial Analysis 会对其单独评测。
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