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大規模な言語モデルで,放射線学のヨーロッパ・ディプロマを解決する:比較評価

Hamza Eren Güzel¹, Laura Oleaga², Ali Murat Koç³

¹Department of Radiology, Ministry of Health Izmir City Hospital, İzmir, Turkey (H.E.G.).

Academic radiology

|February 14, 2026

まとめ

2つの高度なAIモデルであるGPT-5とGemini 2.5 Proは,欧州放射線学学士号 (EDiR) 試験に合格し,放射線学教育の可能性を示しています. しかし,完全な臨床展開のために視覚的および時間的な理解に課題が残っています.

科学分野:

医療イメージングにおける人工知能
放射線学教育テクノロジーテクノロジー
大型言語モデル (LLM) とは

背景:

マルチモダルの大型言語モデル (LLM) は急速に進歩しており,放射線学の教育と評価において大きな可能性を秘めている.
標準化された医療検査でこれらのLLMのパフォーマンスを評価することは,その能力と限界を理解するために不可欠です.

研究の目的:

3つの最先端のLLM:GPT-5,Gemini 2.5 Pro,およびClaude 4.5 Sonnetのパフォーマンスを比較する.
引退した欧州放射線学学士号 (EDiR) 試験を使用して,彼らの能力を評価する.

主な方法:

EDiRの試験は,複数の応答質問,ショートケース,COREケースを含むもので,LLMにゼロショット条件で実施されました.
入力はテキスト,静的画像,ビデオで構成され,回答は公式の欧州放射線学評議会の基準に基づいて得点されました.
通過する値は,過去の人間のパフォーマンスデータに基づいています.

主要な成果:

Gemini 2.5 Pro (72.6%) とGPT-5 (67.3%) は書面コンポーネントパスマーク (50.9%) を上回ったが,Claude 4.5 Sonnetは47.5%のスコアを獲得した.
GPT-5 (62.5%) とGemini 2.5 Pro (56.3%) は,クロード 4.5 ソネット (50.7%) と異なり,COREコンポーネント (55%のパスマーク) を通過しました.
モデルはテキストと静的な画像のタスクに優れていたが,特定の座標のローカライゼーションとダイナミックなビデオ解釈に苦労した.

結論:

GPT-5とGemini 2.5 Proは,EDiRの合格基準を満たしており,放射線学教育の拡張に適していることを示しています.
セマンティック処理の進歩にもかかわらず,空間的局所化と時間的推論の限界は依然として存在し,自律的な臨床展開を妨げています.
臨床的視覚的アグラウンドは,放射線学の現在のLLMにとって重要な課題です.

キーワード:

人工知能 (AI) とは,人工知能 (AI) のことです.放射線学のヨーロッパ・ディプロマ大規模な言語モデル医療教育医療教育について業績評価の評価について

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