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人工智能幕后:PubMed 的最佳匹配算法。

Artificial intelligence behind the scenes: PubMed's Best Match algorithm.

出版信息

J Med Libr Assoc. 2022 Jan 1;110(1):15-22. doi: 10.5195/jmla.2022.1236.

Abstract

This article focuses on PubMed's Best Match sorting algorithm, presenting a simplified explanation of how it operates and highlighting how artificial intelligence affects search results in ways that are not seen by users. We further discuss user search behaviors and the ethical implications of algorithms, specifically for health care practitioners. PubMed recently began using artificial intelligence to improve the sorting of search results using a Best Match option. In 2020, PubMed deployed this algorithm as the default search method, necessitating serious discussion around the ethics of this and similar algorithms, as users do not always know when an algorithm uses artificial intelligence, what artificial intelligence is, and how it may impact their everyday tasks. These implications resonate strongly in health care, in which the speed and relevancy of search results is crucial but does not negate the importance of a lack of bias in how those search results are selected or presented to the user. As a health care provider will not often venture past the first few results in search of a clinical decision, will Best Match help them find the answers they need more quickly? Or will the algorithm bias their results, leading to the potential suppression of more recent or relevant results?

摘要

本文聚焦于 PubMed 的最佳匹配排序算法,对其工作原理进行简化解释,并着重探讨人工智能如何以用户无法察觉的方式影响搜索结果。我们进一步讨论了用户的搜索行为和算法的伦理道德问题,特别是对医疗保健从业者的影响。PubMed 最近开始使用人工智能通过最佳匹配选项来改进搜索结果的排序。2020 年,PubMed 将该算法作为默认搜索方法,这就需要围绕该算法和类似算法的伦理道德展开严肃讨论,因为用户并不总是知道算法何时使用了人工智能、人工智能是什么以及它可能如何影响他们的日常任务。这些影响在医疗保健领域中尤为明显,因为搜索结果的速度和相关性至关重要,但这并不能否定在选择或向用户呈现搜索结果时避免偏见的重要性。由于医疗保健提供者在寻找临床决策时通常不会深入研究前几个搜索结果,那么最佳匹配是否会帮助他们更快地找到所需答案?还是说该算法会对他们的结果产生偏见,导致最近或更相关的结果被抑制?

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f2a/8830327/d4022b22ceab/jmla-110-1-15-g001.jpg

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