Medical Long Context Reasoning (MLCR-AA)
MLCR-AA Pass Rate
MLCR-AA Completeness
MLCR-AA Accuracy
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。保険・医療案件を確認する請求担当者と同じように、AIモデルが長く断片化された医療記録を推論できるかを測ります。各ケースは約25,000〜64,000トークンの合成医療記録です。問題は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段階の難易度に分かれており、MLCR-AAは最も難しい2段階を使用します。
3つのLLM審査員からなるパネルが、各回答を専門家が注釈した正解データと元の診療記録に照らして確認します。空の回答は審査されず、不合格として扱われます。主要スコアは合格率で、過半数の審査員がそれぞれ、回答が完全で記録と矛盾する記述がないと判断した場合に合格となります。専門家レベルおよび複合的な多段階（難問）の問題セットについて、合格率と正確性、完全性を報告しています。
Claude Sonnet 5.5 (Max, Default Fallback)が合格率41.7%でMLCR-AAの最高スコアです。 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、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は非公開のケースでこの作業を評価するため、医療記録の確認、保険請求、その他の長文書を扱う医療業務に市販モデルを使う組織に関係があります。
いいえ。MLCR-AAは独立した評価であり、Artificial Analysis Intelligence Indexには寄与しません。
評価を探す
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