Medical Long Context Reasoning (MLCR-AA)
MLCR-AA Score
MLCR-AA Accuracy (Judged Responses)
MLCR-AA Completeness (Judged Responses)
MLCR-AA Conciseness
スコア
MLCR-AA：スコア
MLCR-AA：スコア vs. タスクあたりのコスト
Medical Long Context Reasoning（MLCR-AA）の評価済み回答の正確性と完全性
トークン使用量
MLCR-AA：タスクあたりの出力トークン
コスト
MLCR-AA：タスクあたりのコスト
速度
MLCR-AA：タスクあたりの時間
スコア vs. リリース日
MLCR-AA：スコア vs. リリース日
タスク例
よくある質問
MLCR-AAは、Wisedocsが公開するMLCRをArtificial Analysisが評価したものです。長く断片化された医療記録をAIモデルが推論し、保険・医療案件を確認する請求担当者のように複数文書を統合できるかを測ります。ケースは約25,000〜64,000トークンの合成医療記録で、単一事実の特定から専門家レベルの臨床統合、複合的な多段階推論まで6段階の難易度に分類されます。
各回答はまず簡潔性の基準を満たす必要があり、参照回答の5倍を超える長さの回答は0点です。簡潔な回答は3つのLLM審査員が評価し、正確性と完全性を多数決で判定します。主要な総合合格率では、回答が簡潔で、完全かつ正確と判定された場合のみ加点されます。このページの主要スコアは、専門家レベルおよび複合的な多段階（難問）セットでの総合合格率です。
MLCR-AAの結果が公開されているモデルの中で、Claude Sonnet 5.5 (Adaptive Reasoning, Max Effort, Default Fallback)が総合合格率75%で最高スコアです。 モデルを見る
数百回の受診にわたる記録を確認する請求担当者は、単一の事実を探すだけでなく、断片化された文書から時系列、因果関係、治療パターン、請求との関連性を再構築します。MLCR-AAはまさにこの能力を測定し、医療記録の確認、保険請求、その他の長文書を扱う医療ワークフロー向けに市販AIを評価する組織にとって重要です。
いいえ。MLCR-AAは独立した評価として報告され、Artificial Analysis Intelligence Indexには寄与しません。Artificial Analysisが個別に評価しています。
評価を探す
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