Discipline of Medical Imaging Sciences, Sydney School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2006, Australia; Medical Imaging Optimisation Perception Group, Discipline of Medical Imaging Sciences, Sydney School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2006, Australia.
Eur J Radiol. 2023 Sep;166:111013. doi: 10.1016/j.ejrad.2023.111013. Epub 2023 Jul 25.
Image interpretation is a fundamental aspect of radiology. The treatment and management of patients relies on accurate and timely imaging diagnosis. However, errors in radiological reports can negatively impact on patient health outcomes. These misdiagnoses can be caused by several different errors, but cognitive biases account for 74 % of all image interpretation errors. There are many biases that can impact on a radiologist's perception and cognitive processes. Several recent narrative reviews have discussed these cognitive biases and have offered possible strategies to mitigate their effects. However, these strategies remain untested. Therefore, the purpose of this scoping review is to evaluate the current knowledge on the extent that cognitive biases impact on medical image interpretation.
Scopus and Medline Databases were searched using relevant keywords to identify papers published between 2012 and 2022. A subsequent hand search of the narrative reviews was also performed. All studies collected were screened and assessed against the inclusion and exclusion criteria.
Twenty-four publications were included and categorised into five main themes: satisfaction of search, availability bias, hindsight bias, framing bias and other biases. From these studies, there were mixed results regarding the impact of cognitive biases, highlighting the need for further investigation in this area. Moreover, the limited and untested debiasing methods offered by a minority of the publications and narrative reviews also suggests the need for further research. The potential of role of artificial intelligence is also highlighted to further assist radiologists in identifying and mitigating these cognitive biases.
Cognitive biases can impact radiologists' image interpretation, however the effectiveness of debiasing strategies remain largely untested.
影像解读是放射学的一个基本方面。患者的治疗和管理依赖于准确和及时的影像学诊断。然而,放射报告中的错误会对患者的健康结果产生负面影响。这些误诊可能是由多种不同的错误引起的,但认知偏差占所有影像解读错误的 74%。有许多偏见会影响放射科医生的感知和认知过程。最近有几项叙述性综述讨论了这些认知偏差,并提出了可能减轻其影响的策略。然而,这些策略仍未经测试。因此,本范围综述的目的是评估认知偏差对医学影像解读的影响程度的现有知识。
使用相关关键词在 Scopus 和 Medline 数据库中进行搜索,以确定 2012 年至 2022 年期间发表的论文。随后还对手头的叙述性综述进行了搜索。对收集到的所有研究进行了筛选,并根据纳入和排除标准进行了评估。
共纳入 24 篇出版物,并分为五个主要主题:搜索满意度、可用性偏差、后见之明偏差、框架偏差和其他偏差。从这些研究中,关于认知偏差的影响存在混合结果,这突出表明需要在这一领域进行进一步调查。此外,少数出版物和叙述性综述提供的有限且未经测试的去偏方法也表明需要进一步研究。还强调了人工智能的潜在作用,可以进一步帮助放射科医生识别和减轻这些认知偏差。
认知偏差会影响放射科医生的影像解读,但是去偏策略的有效性在很大程度上仍未经测试。