Human-computer collaboration for skin cancer recognition
Human-computer collaboration for skin cancer recognition
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DOI：
10.1038/s41591-020-0942-0
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发表时间：
2020-06-22
期刊：
影响因子：
82.9
通讯作者：
Kittler, Harald
中科院分区：
文献类型：
--
作者：
Tschandl, Philipp;Rinner, Christoph;Kittler, Harald
远程医疗的快速增长，加上诊断人工智能（AI）的最新进展，使我们必须考虑将基于人工智能的支持纳入新的护理模式的机会和风险。在这里，我们建立在基于图像的AI用于皮肤癌诊断的准确性方面的最新成就，以解决基于AI的支持在不同临床专业知识水平和多个临床工作流程中的不同表示的影响。我们发现，高质量的基于人工智能的临床决策支持比单独使用人工智能或医生都能提高诊断准确性，而且经验最少的临床医生从基于人工智能的支持中获益最多。我们进一步发现，在移动技术环境中，基于人工智能的多类概率优于基于内容的图像检索（CBIR）表示，并且基于人工智能的支持在模拟第二意见和远程医疗分诊方面具有实用性。除了证明在非专业临床医生手中使用高质量人工智能的潜在好处外，我们还发现，有缺陷的人工智能可能会误导包括专家在内的整个临床医生。最后，我们展示了来自人工智能类别激活图的见解可以为人类诊断的改进提供信息。总之，我们的方法和发现为未来的基于图像的诊断研究提供了一个框架，以改善临床实践中的人机协作。对基于人工智能的决策支持在皮肤肿瘤诊断中的价值进行的系统评估表明，人机协作优于每种单独的方法，并支持自动化方法在诊断医学中的潜力。
The rapid increase in telemedicine coupled with recent advances in diagnostic artificial intelligence (AI) create the imperative to consider the opportunities and risks of inserting AI-based support into new paradigms of care. Here we build on recent achievements in the accuracy of image-based AI for skin cancer diagnosis to address the effects of varied representations of AI-based support across different levels of clinical expertise and multiple clinical workflows. We find that good quality AI-based support of clinical decision-making improves diagnostic accuracy over that of either AI or physicians alone, and that the least experienced clinicians gain the most from AI-based support. We further find that AI-based multiclass probabilities outperformed content-based image retrieval (CBIR) representations of AI in the mobile technology environment, and AI-based support had utility in simulations of second opinions and of telemedicine triage. In addition to demonstrating the potential benefits associated with good quality AI in the hands of non-expert clinicians, we find that faulty AI can mislead the entire spectrum of clinicians, including experts. Lastly, we show that insights derived from AI class-activation maps can inform improvements in human diagnosis. Together, our approach and findings offer a framework for future studies across the spectrum of image-based diagnostics to improve human-computer collaboration in clinical practice.A systematic evaluation of the value of AI-based decision support in skin tumor diagnosis demonstrates the superiority of human-computer collaboration over each individual approach and supports the potential of automated approaches in diagnostic medicine.