• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

相似文献

1
Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint.对医学人工智能革命的期望调适:医学实习生的观点
JMIR Med Inform. 2022 Aug 15;10(8):e34304. doi: 10.2196/34304.
2
Ethical considerations for the use of artificial intelligence in medical decision-making capacity assessments.人工智能在医学决策能力评估中应用的伦理考量。
Psychiatry Res. 2023 Oct;328:115466. doi: 10.1016/j.psychres.2023.115466. Epub 2023 Sep 7.
3
The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare.人工智能的前景:人工智能在医疗保健领域的机遇与挑战综述。
Br Med Bull. 2021 Sep 10;139(1):4-15. doi: 10.1093/bmb/ldab016.
4
Roles and Competencies of Doctors in Artificial Intelligence Implementation: Qualitative Analysis Through Physician Interviews.人工智能实施中医生的角色与能力:通过医生访谈进行的定性分析
JMIR Form Res. 2023 May 18;7:e46020. doi: 10.2196/46020.
5
Integrating Artificial and Human Intelligence: A Partnership for Responsible Innovation in Biomedical Engineering and Medicine.人工智能与人类智能的融合:生物医学工程和医学领域负责任创新的合作伙伴关系。
OMICS. 2020 May;24(5):247-263. doi: 10.1089/omi.2019.0038. Epub 2019 Jul 16.
6
Perceptions of Family Physicians About Applying AI in Primary Health Care: Case Study From a Premier Health Care Organization.家庭医生对人工智能在初级卫生保健中应用的看法:来自一家顶级医疗保健机构的案例研究。
JMIR AI. 2024 Apr 17;3:e40781. doi: 10.2196/40781.
7
ARTIFICIAL INTELLIGENCE IN MEDICAL PRACTICE: REGULATIVE ISSUES AND PERSPECTIVES.人工智能在医学实践中的应用:监管问题与展望。
Wiad Lek. 2020;73(12 cz 2):2722-2727.
8
Your Robot Therapist Will See You Now: Ethical Implications of Embodied Artificial Intelligence in Psychiatry, Psychology, and Psychotherapy.您的机器人治疗师现在为您服务:具身人工智能在精神病学、心理学和心理治疗中的伦理意义。
J Med Internet Res. 2019 May 9;21(5):e13216. doi: 10.2196/13216.
9
The medical profession transformed by artificial intelligence: Qualitative study.人工智能对医学专业的变革:定性研究
Digit Health. 2022 Dec 13;8:20552076221143903. doi: 10.1177/20552076221143903. eCollection 2022 Jan-Dec.
10
Artificial Intelligence and Its Role in the Management of Chronic Medical Conditions: A Systematic Review.人工智能及其在慢性疾病管理中的作用:一项系统综述。
Cureus. 2023 Sep 27;15(9):e46066. doi: 10.7759/cureus.46066. eCollection 2023 Sep.

引用本文的文献

1
The Role of Machine Learning in the Detection of Cardiac Fibrosis in Electrocardiograms: Scoping Review.机器学习在心电图检测心脏纤维化中的作用:范围综述
JMIR Cardio. 2024 Dec 30;8:e60697. doi: 10.2196/60697.
2
Ethical issues of the use of AI-driven mobile apps for education.人工智能驱动的移动应用程序在教育中使用的伦理问题。
Front Public Health. 2023 Jan 11;10:1118116. doi: 10.3389/fpubh.2022.1118116. eCollection 2022.

本文引用的文献

1
External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review.用于放射诊断的深度学习算法的外部验证:一项系统评价。
Radiol Artif Intell. 2022 May 4;4(3):e210064. doi: 10.1148/ryai.210064. eCollection 2022 May.
2
AI recognition of patient race in medical imaging: a modelling study.人工智能识别医学影像中的患者种族:一项建模研究。
Lancet Digit Health. 2022 Jun;4(6):e406-e414. doi: 10.1016/S2589-7500(22)00063-2. Epub 2022 May 11.
3
Health Care Students' Perspectives on Artificial Intelligence: Countrywide Survey in Canada.医护专业学生对人工智能的看法:加拿大全国性调查
JMIR Med Educ. 2022 Jan 31;8(1):e33390. doi: 10.2196/33390.
4
Artificial intelligence in radiology: 100 commercially available products and their scientific evidence.放射学中的人工智能:100种商用产品及其科学证据。
Eur Radiol. 2021 Jun;31(6):3797-3804. doi: 10.1007/s00330-021-07892-z. Epub 2021 Apr 15.
5
Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis.深度学习在医学成像中的诊断准确性:一项系统评价与荟萃分析。
NPJ Digit Med. 2021 Apr 7;4(1):65. doi: 10.1038/s41746-021-00438-z.
6
How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals.医学人工智能设备的评估方式:基于对美国食品药品监督管理局批准情况分析的局限性与建议
Nat Med. 2021 Apr;27(4):582-584. doi: 10.1038/s41591-021-01312-x.
7
Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study.关于美国食品药品监督管理局(FDA)基于人工智能/机器学习的医疗器械修改框架的公众意见中的行业关系与证据:一项横断面研究
BMJ Open. 2020 Oct 14;10(10):e039969. doi: 10.1136/bmjopen-2020-039969.
8
The future of digital health with federated learning.联合学习助力数字健康的未来。
NPJ Digit Med. 2020 Sep 14;3:119. doi: 10.1038/s41746-020-00323-1. eCollection 2020.
9
The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database.基于人工智能且获美国食品药品监督管理局批准的医疗设备及算法的现状:一个在线数据库。
NPJ Digit Med. 2020 Sep 11;3:118. doi: 10.1038/s41746-020-00324-0. eCollection 2020.
10
Current Challenges and Barriers to Real-World Artificial Intelligence Adoption for the Healthcare System, Provider, and the Patient.医疗系统、医疗服务提供者及患者在实际应用人工智能方面当前面临的挑战与障碍
Transl Vis Sci Technol. 2020 Aug 11;9(2):45. doi: 10.1167/tvst.9.2.45. eCollection 2020 Aug.

对医学人工智能革命的期望调适:医学实习生的观点

Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint.

作者信息

Hu Zoe, Hu Ricky, Yau Olivia, Teng Minnie, Wang Patrick, Hu Grace, Singla Rohit

机构信息

School of Medicine, Queen's University, Kingston, ON, Canada.

School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada.

出版信息

JMIR Med Inform. 2022 Aug 15;10(8):e34304. doi: 10.2196/34304.

DOI:10.2196/34304
PMID:35969464
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9425164/
Abstract

The rapid development of artificial intelligence (AI) in medicine has resulted in an increased number of applications deployed in clinical trials. AI tools have been developed with goals of improving diagnostic accuracy, workflow efficiency through automation, and discovery of novel features in clinical data. There is subsequent concern on the role of AI in replacing existing tasks traditionally entrusted to physicians. This has implications for medical trainees who may make decisions based on the perception of how disruptive AI may be to their future career. This commentary discusses current barriers to AI adoption to moderate concerns of the role of AI in the clinical setting, particularly as a standalone tool that replaces physicians. Technical limitations of AI include generalizability of performance and deficits in existing infrastructure to accommodate data, both of which are less obvious in pilot studies, where high performance is achieved in a controlled data processing environment. Economic limitations include rigorous regulatory requirements to deploy medical devices safely, particularly if AI is to replace human decision-making. Ethical guidelines are also required in the event of dysfunction to identify responsibility of the developer of the tool, health care authority, and patient. The consequences are apparent when identifying the scope of existing AI tools, most of which aim to be physician assisting rather than a physician replacement. The combination of the limitations will delay the onset of ubiquitous AI tools that perform standalone clinical tasks. The role of the physician likely remains paramount to clinical decision-making in the near future.

摘要

人工智能(AI)在医学领域的迅速发展导致了越来越多的应用被部署到临床试验中。开发人工智能工具的目标是提高诊断准确性、通过自动化提高工作流程效率以及发现临床数据中的新特征。随后人们担心人工智能在取代传统上由医生承担的现有任务方面所起的作用。这对医学实习生有影响,他们可能会基于对人工智能对其未来职业可能造成的干扰程度的认知来做出决策。本评论讨论了当前人工智能应用面临的障碍,以缓解人们对人工智能在临床环境中作用的担忧,特别是作为取代医生的独立工具的作用。人工智能的技术局限性包括性能的可推广性以及现有基础设施在容纳数据方面的不足,这两点在试点研究中不太明显,因为在试点研究中,在受控的数据处理环境中能实现高性能。经济局限性包括安全部署医疗设备的严格监管要求,特别是如果人工智能要取代人类决策的话。如果出现功能失调情况,还需要道德准则来确定工具开发者、医疗保健机构和患者的责任。在确定现有人工智能工具的范围时,后果很明显,其中大多数旨在辅助医生而非取代医生。这些局限性的综合作用将推迟能执行独立临床任务的普及型人工智能工具的出现。在不久的将来,医生在临床决策中的作用可能仍然至关重要。