医学における大型言語モデルの作成と採用

Nigam H Shah^1,2,3, David Entwistle¹, Michael A Pfeffer^1,2

¹Stanford Health Care, Palo Alto, California.

JAMA

|August 7, 2023

まとめ

大規模な言語モデル (LLM) は医療上の利点がありますが,積極的な関与が不可欠です. 安全で効果的な医療への統合を保証するために医療データと現実世界のテストで LLMの開発を導く必要があります

科学分野:

医療情報学
医療における人工知能
臨床的意思決定支援

背景:

医学的な応用のための大型言語モデル (LLM) に関する関心が高まっています.
医学における現在のLLMアプリケーションには,特定の医療訓練データと利益の検証が欠けていることが多い.
積極的に関わらなければ,LLMの統合を形作る権限を失うリスク.

研究の目的:

医学におけるLLMの使用の形成に積極的に関与する必要性を強調する.
医療機器の責任ある開発と導入の重要性を強調する.
人工知能を医療に統合する積極的なアプローチを提唱する.

主な方法:

医療業務におけるLLMの採用の現在の傾向を分析する.
医療のためのLLMトレーニングと検証のギャップを特定する.
LLMの開発と展開の積極的な形成のための枠組み提案

主要な成果:

LLMのアプリケーションは,適切な医療データ訓練や利益の検証なしに,医療業務にますます使用されています.
パッシブな観察は LLMが医療実践をどう形づくるか制御を譲る危険性があります
アクティブ・シェーピングは,関連するデータを提供し,利益を定義し,現実世界の評価を必要とします.

結論:

医学におけるLLMの恩恵を責任をもって活用するためには積極的に関与することが不可欠です
医療データの提供,利益の特定,実際のテストは安全なLLM統合に不可欠です.
プロアクティブ・シェーピングは医学的な実践を妨げるのではなく強化する役割を果たします

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