AI診斷力追上醫生?大阪大學研究話你知!

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生成式人工智能的診斷能力可媲美非專科醫生

一項針對醫學研究的元分析顯示,生成式人工智能在診斷方面的準確性與非專科醫生相當。

在醫學領域,生成式人工智能的診斷應用引起了廣泛關注,許多研究論文已經發表。然而,由於每項研究的評估標準不同,因此需要進行全面分析,以確定人工智能在實際醫療環境中的應用範圍及其相對於醫生的優勢。

由大阪市立大學醫學研究所的高田宏貴博士和上田大樹副教授領導的研究小組,對2018年6月至2024年6月間發表的83篇涵蓋多個醫療專科的研究論文進行了元分析。被分析的多種大型語言模型中,ChatGPT是被研究最頻繁的。

比較評估顯示,醫療專家診斷的準確性比生成式人工智能高出15.8%。生成式人工智能的平均診斷準確性為52.1%,而最新的生成式人工智能模型有時顯示出與非專科醫生相當的準確性。

高田博士表示:「這項研究顯示生成式人工智能的診斷能力可媲美非專科醫生。它可以用於醫療教育,以支持非專科醫生並協助在資源有限的地區進行診斷。」他補充道:「進一步的研究,例如在更複雜的臨床場景中進行評估、使用實際醫療記錄進行性能評估、提高人工智能決策的透明度,以及在不同患者群體中進行驗證,都是確認人工智能能力所需的。」

這項研究的結果已發表於《npj Digital Medicine》期刊。

### 編輯評論

這項研究揭示了生成式人工智能在醫療診斷中的巨大潛力,特別是在資源短缺的地區。隨著技術的進步,未來我們可能會看到這些工具在醫療系統中扮演更為重要的角色。值得注意的是,雖然生成式人工智能的診斷準確性已經達到與非專科醫生相當的水平,但仍需謹慎對待其在複雜病例中的應用。醫療界應該考慮如何有效地將這些工具整合進現有的診療流程中,以提高醫療服務的可及性和效率。同時,透明的決策過程和對患者群體的驗證將是未來研究的重要方向,這不僅能增強醫療信任,也能確保患者安全。

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