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本文引用的文献

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Explainable artificial intelligence (XAI) in deep learning-based medical image analysis.深度学习在医学影像分析中的可解释人工智能(XAI)。
Med Image Anal. 2022 Jul;79:102470. doi: 10.1016/j.media.2022.102470. Epub 2022 May 4.
2
A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology.一项关于临床医生在眼科、皮肤科、放射科和放射肿瘤学中使用人工智能情况的调查。
Sci Rep. 2021 Mar 4;11(1):5193. doi: 10.1038/s41598-021-84698-5.
3
Human-computer collaboration for skin cancer recognition.人机协作进行皮肤癌识别。
Nat Med. 2020 Aug;26(8):1229-1234. doi: 10.1038/s41591-020-0942-0. Epub 2020 Jun 22.
4
Investigating the Barriers to Physician Adoption of an Artificial Intelligence- Based Decision Support System in Emergency Care: An Interpretative Qualitative Study.探究急诊护理中医师采用基于人工智能的决策支持系统的障碍:一项诠释性定性研究。
Stud Health Technol Inform. 2020 Jun 16;270:1001-1005. doi: 10.3233/SHTI200312.
5
Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians.医疗保健中的人工智能与人类信任:聚焦临床医生
J Med Internet Res. 2020 Jun 19;22(6):e15154. doi: 10.2196/15154.
6
Attitudes Toward Artificial Intelligence Among Radiologists, IT Specialists, and Industry.放射科医生、IT 专家和行业对人工智能的态度。
Acad Radiol. 2021 Jun;28(6):834-840. doi: 10.1016/j.acra.2020.04.011. Epub 2020 May 13.
7
Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study.人工智能在前列腺癌活检中的诊断和分级:一项基于人群的诊断研究。
Lancet Oncol. 2020 Feb;21(2):222-232. doi: 10.1016/S1470-2045(19)30738-7. Epub 2020 Jan 8.
8
Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology.人工智能在数字病理学中的应用——诊断和精准肿瘤学的新工具。
Nat Rev Clin Oncol. 2019 Nov;16(11):703-715. doi: 10.1038/s41571-019-0252-y. Epub 2019 Aug 9.
9
Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer.一种用于改善前列腺癌Gleason评分的深度学习算法的开发与验证
NPJ Digit Med. 2019 Jun 7;2:48. doi: 10.1038/s41746-019-0112-2. eCollection 2019.
10
Physician perspectives on integration of artificial intelligence into diagnostic pathology.医生对人工智能融入诊断病理学的看法。
NPJ Digit Med. 2019 Apr 26;2:28. doi: 10.1038/s41746-019-0106-0. eCollection 2019.

人工智能对病理学家决策的影响:一项实验。

Impact of artificial intelligence on pathologists' decisions: an experiment.

机构信息

School of Health Services Management, Ted Rogers School of Management, Toronto, Ontario, Canada.

Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, Ontario, Canada.

出版信息

J Am Med Inform Assoc. 2022 Sep 12;29(10):1688-1695. doi: 10.1093/jamia/ocac103.

DOI:10.1093/jamia/ocac103
PMID:35751441
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9471707/
Abstract

OBJECTIVE

The accuracy of artificial intelligence (AI) in medicine and in pathology in particular has made major progress but little is known on how much these algorithms will influence pathologists' decisions in practice. The objective of this paper is to determine the reliance of pathologists on AI and to investigate whether providing information on AI impacts this reliance.

MATERIALS AND METHODS

The experiment using an online survey design. Under 3 conditions, 116 pathologists and pathology students were tasked with assessing the Gleason grade for a series of 12 prostate biopsies: (1) without AI recommendations, (2) with AI recommendations, and (3) with AI recommendations accompanied by information about the algorithm itself, specifically algorithm accuracy rate and algorithm decision-making process.

RESULTS

Participant responses were significantly more accurate with the AI decision aids than without (92% vs 87%, odds ratio 13.30, P < .01). Unexpectedly, the provision of information on the algorithm made no significant difference compared to AI without information. The reliance on AI correlated with general beliefs on AI's usefulness but not with particular assessments of the AI tool offered. Decisions were made faster when AI was provided.

DISCUSSION

These results suggest that pathologists are willing to rely on AI regardless of accuracy or explanations. Generalization beyond the specific tasks and explanations provided will require further studies.

CONCLUSION

This study suggests that the factors that influence the reliance on AI differ in practice from beliefs expressed by clinicians in surveys. Implementation of AI in prospective settings should take individual behaviors into account.

摘要

目的

人工智能(AI)在医学领域,尤其是在病理学领域的准确性已经取得了重大进展,但人们对这些算法在实践中对病理学家决策的影响程度知之甚少。本文旨在确定病理学家对 AI 的依赖程度,并研究提供 AI 相关信息是否会影响这种依赖。

材料与方法

本实验采用在线调查设计。在 3 种条件下,116 名病理学家和病理学生被要求对一系列 12 份前列腺活检的 Gleason 分级进行评估:(1)无 AI 建议,(2)有 AI 建议,(3)有 AI 建议并附有关于算法本身的信息,具体包括算法准确率和算法决策过程。

结果

与没有 AI 决策辅助时相比,参与者的回答在使用 AI 决策辅助时明显更准确(92%对 87%,优势比 13.30,P < 0.01)。出乎意料的是,与没有提供 AI 信息相比,提供关于算法的信息没有显著差异。对 AI 的依赖程度与对 AI 有用性的一般信念相关,但与提供的特定 AI 工具评估无关。提供 AI 时,决策速度更快。

讨论

这些结果表明,病理学家愿意依赖 AI,无论其准确性或解释如何。要将其推广到提供的具体任务和解释之外,还需要进一步的研究。

结论

本研究表明,在实践中,影响对 AI 依赖的因素与临床医生在调查中表达的信念不同。在前瞻性设置中实施 AI 应考虑个体行为。