Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations.
Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations.
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DOI：
10.1038/s41416-021-01333-w
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发表时间：
2021-07
影响因子：
8.8
通讯作者：
Gilbert FJ
中科院分区：
文献类型：
--
作者：
Hickman SE;Baxter GC;Gilbert FJ
回顾性研究表明，人工智能 (AI) 算法可以匹配并提高放射科医生在乳腺筛查方面的表现。这些工具可以帮助完成人类无法完成的任务，例如自动分类患者和预测治疗结果。随着成像需求的指数级增长以及预计提供报告的劳动力的减少，乳腺成像面临着越来越大的压力。人们越来越关注采用人工智能来提高工作流程效率和患者治疗效果，从而寻求缓解这些压力的解决方案。在将人工智能算法纳入医疗保健系统之前和之后，需要大量数据来测试和监控人工智能算法。尽管正在制定策略来利用医疗机构内已有的数据，但目前数据的可用性仍然有限。将人工智能应用于日常乳腺成像所面临的挑战不容低估，国家机构提供应对这些挑战的指导，同时考虑社会、工业和医疗保健前景的观点至关重要。除了探索迄今为止已发现的关键伦理、技术、法律和监管挑战之外，这篇综述还提供了人工智能在乳腺成像中的评估和使用的背景。
Retrospective studies have shown artificial intelligence (AI) algorithms can match as well as enhance radiologist’s performance in breast screening. These tools can facilitate tasks not feasible by humans such as the automatic triage of patients and prediction of treatment outcomes. Breast imaging faces growing pressure with the exponential growth in imaging requests and a predicted reduced workforce to provide reports. Solutions to alleviate these pressures are being sought with an increasing interest in the adoption of AI to improve workflow efficiency as well as patient outcomes. Vast quantities of data are needed to test and monitor AI algorithms before and after their incorporation into healthcare systems. Availability of data is currently limited, although strategies are being devised to harness the data that already exists within healthcare institutions. Challenges that underpin the realisation of AI into everyday breast imaging cannot be underestimated and the provision of guidance from national agencies to tackle these challenges, taking into account views from a societal, industrial and healthcare prospective is essential. This review provides background on the evaluation and use of AI in breast imaging in addition to exploring key ethical, technical, legal and regulatory challenges that have been identified so far.
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DOI：
10.1093/jamia/ocaa094
发表时间：
2020-12-01
影响因子：
6.4
作者：
DeCamp, Matthew;Lindvall, Charlotta
通讯作者：
Lindvall, Charlotta
影响因子：
2.4
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影响因子：
5.9
作者：
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影响因子：
7.7
作者：
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通讯作者：
Young, Heather
DOI：
10.1002/jmri.26244
发表时间：
2019-03
期刊：
Journal of magnetic resonance imaging : JMRI
影响因子：
--
作者：
Ha R;Chang P;Mutasa S;Karcich J;Goodman S;Blum E;Kalinsky K;Liu MZ;Jambawalikar S
通讯作者：
Jambawalikar S