本文由小咖机器人翻译整理

期刊来源：Nat Med

原文链接：https://doi.org/10.1038/s41591-024-02850-w

摘要内容如下：

人工智能（AI）在医学图像解释中的集成需要临床医生和AI算法之间的有效协作。尽管先前的研究证明了人工智能在提高临床医生整体表现方面的潜力，但对临床医生的个体影响仍不清楚。这项大规模研究调查了人工智能辅助对140名放射科医生在15项胸部X射线诊断任务中的异质性影响，并确定了这些影响的预测因素。令人惊讶的是，传统的基于经验的因素，如多年的经验、亚专业和对人工智能工具的熟悉程度，无法可靠地预测人工智能辅助的影响。此外，表现较差的放射科医生并不总是从人工智能辅助中获益更多，这对流行的假设提出了挑战。相反，我们发现AI错误的发生会严重影响治疗结果，不准确的AI预测会对放射科医师在所有病理集合和一半的个体病理调查中的表现产生不利影响。我们的研究结果强调了个性化方法对临床医生与人工智能协作的重要性，以及准确的人工智能模型的重要性。通过了解影响人工智能辅助有效性的因素，本研究为有针对性地实施人工智能提供了有价值的见解，使个体临床医生在临床实践中获得最大收益。

英文原文如下：

Abstracts

The integration of artificial intelligence (AI) in medical image interpretation requires effective collaboration between clinicians and AI algorithms. Although previous studies demonstrated the potential of AI assistance in improving overall clinician performance, the individual impact on clinicians remains unclear. This large-scale study examined the heterogeneous effects of AI assistance on 140 radiologists across 15 chest X-ray diagnostic tasks and identified predictors of these effects. Surprisingly, conventional experience-based factors, such as years of experience, subspecialty and familiarity with AI tools, fail to reliably predict the impact of AI assistance. Additionally, lower-performing radiologists do not consistently benefit more from AI assistance, challenging prevailing assumptions. Instead, we found that the occurrence of AI errors strongly influences treatment outcomes, with inaccurate AI predictions adversely affecting radiologist performance on the aggregate of all pathologies and on half of the individual pathologies investigated. Our findings highlight the importance of personalized approaches to clinician-AI collaboration and the importance of accurate AI models. By understanding the factors that shape the effectiveness of AI assistance, this study provides valuable insights for targeted implementation of AI, enabling maximum benefits for individual clinicians in clinical practice.

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