奥地利维也纳医科大学Harald Kittler研究组利用人机协作识别皮肤癌。相关论文于2020年6月22日发表于国际学术期刊《自然-医学》。
他们基于在皮肤癌诊断中基于图像的人工智能（AI）准确性方面的最新成就，来解决不同水平的临床专业知识和多种临床工作流程，对基于AI的支持的各种表示形式的影响。他们发现，高质量的基于AI的临床决策支持比单独使用AI或医师可以提高诊断准确性，并且经验最少的临床医生将从基于AI的支持中获得最大收益。
他们进一步发现，在移动技术环境中，基于AI的多类概率优于基于AI的基于内容的图像检索（CBIR）表示，并且基于AI的支持在模拟第二观点和远程医疗分类中具有实用性。除了在非专家临床医生手中证明与高质量AI相关的潜在好处外，他们还发现，错误的AI可能会误导包括专家在内的整个临床医生。最后，他们证明了从AI类激活图谱获得的见解可以为人类诊断提供参考。
总之，他们的方法和发现为未来基于图像诊断的各种研究提供了一个框架，以改善人机在临床实践中的协作。
据悉，远程医疗的迅猛发展，加上诊断AI的最新发展，必须考虑将基于AI的支持介入新的护理模式中的机遇和风险。
附：英文原文
Title: Human–computer collaboration for skin cancer recognition
Author: Philipp Tschandl, Christoph Rinner, Zoe Apalla, Giuseppe Argenziano, Noel Codella, Allan Halpern, Monika Janda, Aimilios Lallas, Caterina Longo, Josep Malvehy, John Paoli, Susana Puig, Cliff Rosendahl, H. Peter Soyer, Iris Zalaudek, Harald Kittler
Issue&Volume: 2020-06-22
Abstract: 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.
DOI: 10.1038/s41591-020-0942-0
Source: https://www.nature.com/articles/s41591-020-0942-0
Nature Medicine：《自然—医学》，创刊于1995年。隶属于施普林格·自然出版集团，最新IF：87.241
官方网址：https://www.nature.com/nm/
投稿链接：https://mts-nmed.nature.com/cgi-bin/main.plex
本期文章：《自然—医学》：Online/在线发表
