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2
Fully Automated Echocardiogram Interpretation in Clinical Practice.临床实践中的全自动超声心动图解读。
Circulation. 2018 Oct 16;138(16):1623-1635. doi: 10.1161/CIRCULATIONAHA.118.034338.
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Artificial Intelligence in Cardiology.人工智能在心脏病学中的应用。
J Am Coll Cardiol. 2018 Jun 12;71(23):2668-2679. doi: 10.1016/j.jacc.2018.03.521.
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Artificial intelligence in cardiology.心脏病学中的人工智能
Wien Klin Wochenschr. 2017 Dec;129(23-24):866-868. doi: 10.1007/s00508-017-1275-y. Epub 2017 Oct 4.
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Does Machine Learning Automate Moral Hazard and Error?机器学习会使道德风险和错误自动化吗?
Am Econ Rev. 2017 May;107(5):476-480. doi: 10.1257/aer.p20171084.
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Artificial Intelligence in Precision Cardiovascular Medicine.人工智能在精准心血管医学中的应用。
J Am Coll Cardiol. 2017 May 30;69(21):2657-2664. doi: 10.1016/j.jacc.2017.03.571.
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Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MR Imaging Study.三维右心室运动的机器学习可实现肺动脉高压的预后预测:一项心脏磁共振成像研究。
Radiology. 2017 May;283(2):381-390. doi: 10.1148/radiol.2016161315. Epub 2017 Jan 16.
8
Machine Learning for Echocardiographic Imaging: Embarking on Another Incredible Journey.用于超声心动图成像的机器学习:踏上又一段非凡旅程。
J Am Coll Cardiol. 2016 Nov 29;68(21):2296-2298. doi: 10.1016/j.jacc.2016.09.915.
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Using recurrent neural network models for early detection of heart failure onset.使用循环神经网络模型进行心力衰竭发作的早期检测。
J Am Med Inform Assoc. 2017 Mar 1;24(2):361-370. doi: 10.1093/jamia/ocw112.
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Connecting the Dots: From Big Data to Healthy Heart.连点成线:从大数据到健康心脏
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一项基于智能手机的移动健康调查,旨在研究人工智能在心脏病学中的应用。

A smartphone-based survey in mHealth to investigate the introduction of the artificial intelligence into cardiology.

作者信息

Giansanti Daniele, Monoscalco Lisa

机构信息

Centro TISP, ISS, Roma, Italy.

Facoltà di Ingegneria, Università di Tor Vergata, Roma, Italy.

出版信息

Mhealth. 2021 Jan 20;7:8. doi: 10.21037/mhealth-19-188. eCollection 2021.

DOI:10.21037/mhealth-19-188
PMID:33634191
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7882260/
Abstract

BACKGROUND

There is an increasing discussion concerning the integration of artificial intelligence (AI) into medical decision-making. AI science is a branch of engineering that implements novel concepts to resolve complex challenges and defined as the theory and development of computer systems to perform tasks which would normally require human intelligence. AI could aid cardiologists in improving decision-making, workflow, productivity, cost-effectiveness, and ultimately, patient outcomes. The present study proposes a tool for a positioning exercise in cardiology using mobile technology.

METHODS

This study is based on a dedicated tool with electronic surveys that collect the opinions, requirements, and desires of the interested actors including both laypeople and professionals.

RESULTS

The tool was tested on 30 cardiologists and 30 subjects not involved in health care. The data-analysis revealed several clear trends on the cardiologists: (I) a high desire to invest in AI; (II) high confidence in the use of AI in several fields of cardiology from risk prevention to diagnostics in medical imaging; (III) low confidence in the use of AI in quality control procedures; (IV) a strong belief that ethical issues are hampering the diffusion of AI to different fields. The data-analysis on the 30 subjects not involved in health care highlighted that AI is still not well known and therefore looked with suspicious.

CONCLUSIONS

The integration of AI with telemedicine and e-health is a key issue for the health care. The study highlights how the mobile technology-based positioning exercises in mHealth can be useful for health care decision makers.

摘要

背景

关于将人工智能(AI)整合到医疗决策中的讨论日益增多。人工智能科学是工程学的一个分支,它运用新颖的概念来解决复杂的挑战,被定义为计算机系统的理论与开发,以执行通常需要人类智能才能完成的任务。人工智能可以帮助心脏病专家改善决策、工作流程、提高生产力、提升成本效益,并最终改善患者的治疗效果。本研究提出了一种利用移动技术进行心脏病学定位练习的工具。

方法

本研究基于一个配备电子调查问卷的专用工具,该工具收集包括外行和专业人员在内的相关人员的意见、需求和期望。

结果

该工具在30名心脏病专家和30名非医疗保健人员中进行了测试。数据分析揭示了心脏病专家的几个明显趋势:(I)对投资人工智能有很高的意愿;(II)对人工智能在心脏病学的几个领域(从风险预防到医学成像诊断)的应用有很高的信心;(III)对人工智能在质量控制程序中的应用信心不足;(IV)坚信伦理问题阻碍了人工智能在不同领域的推广。对30名非医疗保健人员的数据分析表明,人工智能仍然不太为人所知,因此受到怀疑。

结论

人工智能与远程医疗和电子健康的整合是医疗保健的一个关键问题。该研究强调了移动健康中基于移动技术的定位练习对医疗保健决策者可能是有用的。