Collaborative strategies for deploying artificial intelligence to complement physician diagnoses of acute respiratory distress syndrome.
Collaborative strategies for deploying artificial intelligence to complement physician diagnoses of acute respiratory distress syndrome.
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DOI：
10.1038/s41746-023-00797-9
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发表时间：
2023-04-08
影响因子：
15.2
通讯作者：
中科院分区：
文献类型：
--
作者：
描述使用深度学习的人工智能（AI）诊断系统的能力的研究与研究如何或何时将AI系统整合到现实世界的临床实践中以支持医生和改善诊断的努力之间存在越来越大的差距。为了解决这一差距，我们研究了AI模型部署和医生协作的四种潜在策略，以确定它们对诊断准确性的潜在影响。作为一个案例研究，我们研究了一个人工智能模型，该模型经过训练，可以识别胸部X射线图像上的急性呼吸窘迫综合征（ARDS）的发现。虽然该模型在识别ARDS的发现方面优于医生，但有几个原因可以解释为什么全自动ARDS检测在实践中可能不是最佳的或可行的。在测试的几种协作策略中，我们发现，如果AI模型首先审查胸部X光片，如果不确定，则将其交给医生，这种策略可以实现更高的诊断准确性（0.869，95% CI 0.835-0.903）与医生首先审查胸部X线片并在不确定时遵从AI模型的策略相比（0.824，95% CI 0.781-0.862），或医生仅审查胸部X线片（0.808，95% CI 0.767-0.85）或AI模型仅审查胸部X线片（0.847，95% CI 0.806-0.887）的策略。如果AI模型首先审查胸部X光片，这允许AI系统为高达79%的病例做出决策，让医生专注于最具挑战性的胸部X光片子集。
There is a growing gap between studies describing the capabilities of artificial intelligence (AI) diagnostic systems using deep learning versus efforts to investigate how or when to integrate AI systems into a real-world clinical practice to support physicians and improve diagnosis. To address this gap, we investigate four potential strategies for AI model deployment and physician collaboration to determine their potential impact on diagnostic accuracy. As a case study, we examine an AI model trained to identify findings of the acute respiratory distress syndrome (ARDS) on chest X-ray images. While this model outperforms physicians at identifying findings of ARDS, there are several reasons why fully automated ARDS detection may not be optimal nor feasible in practice. Among several collaboration strategies tested, we find that if the AI model first reviews the chest X-ray and defers to a physician if it is uncertain, this strategy achieves a higher diagnostic accuracy (0.869, 95% CI 0.835–0.903) compared to a strategy where a physician reviews a chest X-ray first and defers to an AI model if uncertain (0.824, 95% CI 0.781–0.862), or strategies where the physician reviews the chest X-ray alone (0.808, 95% CI 0.767–0.85) or the AI model reviews the chest X-ray alone (0.847, 95% CI 0.806–0.887). If the AI model reviews a chest X-ray first, this allows the AI system to make decisions for up to 79% of cases, letting physicians focus on the most challenging subsets of chest X-rays.
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影响因子：
15.2
作者：
Esteva A;Chou K;Yeung S;Naik N;Madani A;Mottaghi A;Liu Y;Topol E;Dean J;Socher R
通讯作者：
Socher R
影响因子：
9.6
作者：
Sjoding, Michael W.;Hofer, Timothy P.;Iwashyna, Theodore J.
通讯作者：
Iwashyna, Theodore J.
影响因子：
82.9
作者：
Tschandl, Philipp;Rinner, Christoph;Kittler, Harald
通讯作者：
Kittler, Harald
影响因子：
3.4
作者：
Andersson, Sabina;Heijl, Anders;Bengtsson, Boel
通讯作者：
Bengtsson, Boel
影响因子：
7.7
作者：
Reamaroon N;Sjoding MW;Lin K;Iwashyna TJ;Najarian K
通讯作者：
Najarian K