熱傷の評価におけるマルチモーダル大規模言語モデルと救急医の比較:前向き非劣性試験
Ahmet Aykut1, Ali Rıza Karayıl2, Cem Yıldırım3
1Department of Emergency Medicine, SBU Van Education and Research Hospital, Van, Türkiye. ahmet.aykut@gmail.com.
Scandinavian journal of trauma, resuscitation and emergency medicine
|February 6, 2026
まとめ
新しいAIモデルは、熱傷面積(体表面積全体)の推定において医師と同等の性能を示したが、熱傷の深達度分類には苦戦した。これは、AIが体表面積推定を支援できる可能性がある一方で、深達度評価には臨床的判断が依然として重要であることを示唆している。
科学分野:
- 医療における人工知能;救急医学診断;医用画像解析
背景:
- 正確な熱傷評価は患者管理に不可欠であるが、臨床医間のばらつきがある。;大規模言語モデルは、救急現場におけるスケーラブルな画像ベースの意思決定支援を提供する可能性がある。;臨床医および専門家基準に対するAIモデルの前向き検証は限られている。
主な方法:
- 三次救急部門における前向き診断精度研究。;急性熱傷領域の標準化された3方向写真をマルチモーダルLLMおよび18人の救急医が分析した。;TBSA推定および熱傷深達度分類のための専門家パネル基準標準との比較。
結論:
- マルチモーダルAIは、救急熱傷ケアにおけるTBSA推定の補助として機能する可能性がある。;熱傷の深達度分類におけるAIのパフォーマンスは現時点では不十分であり、臨床医主導の評価が必要である。;熱傷の深達度評価におけるAIのさらなる開発と外部検証が必要である。
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