マルチモダル大型言語モデルを用いた手腕X線写真によるアトラスアシスト骨年齢推定:比較研究
1Department of Radiology, Kastamonu Training and Research Hospital, Kastamonu 37150, Turkey.
Diagnostics (Basel, Switzerland)
|February 13, 2026
まとめ
ChatGPT-5は,骨の年齢評価における4つのマルチモダルの大型言語モデル (LLM) のうち,臨床的有用性を示し,最高のパフォーマンスを示しました. Gemini 2.5 Pro,Grok-3,Claude 4 Sonnetなどの他のモデルでは,骨の年齢を決定するのに不十分な信頼性が示されました.
科学分野:
- メディカルイマージング (医学イメージング)
- 医療における人工知能
- 小児内分泌学について
背景:
- 骨の年齢の評価は,小児内分泌学と法医学において極めて重要です.
- マルチモダルの大型言語モデル (LLM) は,医学イメージングでは有望ですが,骨の年齢の決定のための評価が必要です.
- ギルサンツ・ラティブ (GR) アトラスは,骨の年齢の評価のための標準的な基準です.
研究 の 目的:
- 4つのマルチモダルのLLM (ChatGPT-5,Gemini 2.5 Pro,Grok-3,Claude 4 Sonnet) の骨年齢決定における診断性能を評価する.
- GRアトラスを用いて,放射線科医の評価とLLMの成績を比較する.
- 骨の年齢評価のためのこれらのLLMの臨床的有用性を決定する.
主な方法:
- 245人の小児手首のX線写真 (年齢18歳未満) の遡及的分析.
- LLMは,骨の年齢の推定のためにGRアトラスアシストドプロンプトを使用しました.
- パフォーマンスメトリックには,平均絶対誤差 (MAE),クラス内相関係数 (ICC),Bland-Altman分析が含まれていました.
- 経験豊富な放射線科医のGRアトラスベースの評価と比較.
主要な成果:
- ChatGPT-5は,最も低いMAE (1.46年) と最高ICC (0.849) を達成し,優れたパフォーマンスを示しました.
- Gemini 2.5 Proは中程度のパフォーマンスを示しました (MAE: 2.24年).
- Grok-3 (MAE: 3.14年) とClaude 4 Sonnet (MAE: 4.29年) は,臨床適用には過度のエラー率を示した.
結論:
- 骨の年齢の評価のためのマルチモダルのLLMの間では,重要な性能の変動が存在します.
- 臨床的有用性の基準を満たしたのはChatGPT-5のみで,補助ツールとして,または監督下での教育支援として可能性を示唆しています.
- ChatGPT-5を除く現在のマルチモダルのLLMは,骨の年齢を臨床的に決定するための信頼性が欠けている.
キーワード:
チャットGPTクラウド・クロード クラウド・クロードとはグーグル・ジェミニ・ジェミニは,グロック・グロック (Grok Grok) とは,グロック (Grok Grok) とは,グロック (Grok Grok) とは,グロック (Grok Grok) とは人工知能 (AI) とは,人工知能 (AI) に関する骨の年齢 骨の年齢大きな言語モデルモデル.放射線学 放射線学さらに関連する動画
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