Human-in-the-loop (HITL) AI may enable an ideal symbiosis of human experts and AI models, harnessing the advantages of both while at the same time overcoming their respective limitations. The purpose of this study was to investigate a novel collective intelligence technology designed to amplify the diagnostic accuracy of networked human groups by forming real-time systems modeled on biological swarms. Using small groups of radiologists, the swarm-based technology was applied to the diagnosis of pneumonia on chest radiographs and compared against human experts alone, as well as two state-of-the-art deep learning AI models. Our work demonstrates that both the swarm-based technology and deep-learning technology achieved superior diagnostic accuracy than the human experts alone. Our work further demonstrates that when used in combination, the swarm-based technology and deep-learning technology outperformed either method alone. The superior diagnostic accuracy of the combined HITL AI solution compared to radiologists and AI alone has broad implications for the surging clinical AI deployment and implementation strategies in future practice.

中文翻译：

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人机与人工智能合作进行胸部X光片诊断。

循环中的（HITL）AI可以实现人类专家和AI模型的理想共生，同时利用两者的优势，同时克服它们各自的局限性。这项研究的目的是研究一种新颖的集体情报技术，该技术旨在通过形成以生物群为模型的实时系统来扩大联网人群的诊断准确性。基于小组的放射科医师将基于群体的技术应用于胸部X光片的肺炎诊断，并与人类专家以及两个先进的深度学习AI模型进行了比较。我们的工作表明，基于群体的技术和深度学习技术均比单独的人类专家获得了更高的诊断准确性。我们的工作进一步证明，当结合使用时，基于群体的技术和深度学习技术仅优于这两种方法。与放射线医生和AI相比，组合式HITL AI解决方案的卓越诊断准确性对未来实践中激增的临床AI部署和实施策略具有广泛的意义。