Radiology in the era of artificial intelligence (AI): Opportunities and challenges.
作者信息
Debnath Jyotindu
机构信息
MG Med, HQ, Uttar Bharat Area, C/o 56 APO, India.
出版信息
Med J Armed Forces India. 2023 Jul-Aug;79(4):369-372. doi: 10.1016/j.mjafi.2023.05.003. Epub 2023 Jun 2.
Abstract
摘要
相似文献
1
Radiology in the era of artificial intelligence (AI): Opportunities and challenges.
Med J Armed Forces India. 2023 Jul-Aug;79(4):369-372. doi: 10.1016/j.mjafi.2023.05.003. Epub 2023 Jun 2.
2
Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success.
J Am Coll Radiol. 2018 Mar;15(3 Pt B):504-508. doi: 10.1016/j.jacr.2017.12.026. Epub 2018 Feb 4.
3
[Artificial Intelligence in Radiology - Definition, Potential and Challenges].
Praxis (Bern 1994). 2021 Jan;110(1):48-53. doi: 10.1024/1661-8157/a003597.
4
Strengths, Weaknesses, Opportunities, and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology.
J Am Coll Radiol. 2019 Sep;16(9 Pt B):1239-1247. doi: 10.1016/j.jacr.2019.05.047.
5
Artificial Intelligence, Augmented Reality, and Virtual Reality Advances and Applications in Interventional Radiology.
Diagnostics (Basel). 2023 Feb 27;13(5):892. doi: 10.3390/diagnostics13050892.
6
Prime Time for Artificial Intelligence in Interventional Radiology.
Cardiovasc Intervent Radiol. 2022 Mar;45(3):283-289. doi: 10.1007/s00270-021-03044-4. Epub 2022 Jan 14.
7
Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging.
Diagnostics (Basel). 2023 Aug 25;13(17):2760. doi: 10.3390/diagnostics13172760.
8
Engineering and clinical use of artificial intelligence (AI) with machine learning and data science advancements: radiology leading the way for future.
Ther Adv Urol. 2021 Sep 20;13:17562872211044880. doi: 10.1177/17562872211044880. eCollection 2021 Jan-Dec.
9
Is Artificial Intelligence the New Friend for Radiologists? A Review Article.
Cureus. 2020 Oct 24;12(10):e11137. doi: 10.7759/cureus.11137.
10
Use of artificial intelligence in emergency radiology: An overview of current applications, challenges, and opportunities.
Clin Imaging. 2022 Sep;89:61-67. doi: 10.1016/j.clinimag.2022.05.010. Epub 2022 May 30.
本文引用的文献
1
Barriers to artificial intelligence implementation in radiology practice: What the radiologist needs to know.
Radiologia (Engl Ed). 2022 Jul-Aug;64(4):324-332. doi: 10.1016/j.rxeng.2022.04.001.
2
Use of artificial intelligence in emergency radiology: An overview of current applications, challenges, and opportunities.
Clin Imaging. 2022 Sep;89:61-67. doi: 10.1016/j.clinimag.2022.05.010. Epub 2022 May 30.
3
Artificial Intelligence in Radiology: an introduction to the most important concepts.
Radiologia (Engl Ed). 2022 May-Jun;64(3):228-236. doi: 10.1016/j.rxeng.2022.03.005.
4
Trend in radiologist workload compared to number of admissions in the emergency department.
Eur J Radiol. 2022 Apr;149:110195. doi: 10.1016/j.ejrad.2022.110195. Epub 2022 Feb 3.
5
Are We Overdoing It? Changes in Diagnostic Imaging Workload during the Years 2010-2020 including the Impact of the SARS-CoV-2 Pandemic.
Healthcare (Basel). 2021 Nov 16;9(11):1557. doi: 10.3390/healthcare9111557.
6
Optimization of Radiology Workflow with Artificial Intelligence.
Radiol Clin North Am. 2021 Nov;59(6):955-966. doi: 10.1016/j.rcl.2021.06.006.
7
Workload for radiologists during on-call hours: dramatic increase in the past 15 years.
Insights Imaging. 2020 Nov 23;11(1):121. doi: 10.1186/s13244-020-00925-z.
8
Workflow Applications of Artificial Intelligence in Radiology and an Overview of Available Tools.
J Am Coll Radiol. 2020 Nov;17(11):1363-1370. doi: 10.1016/j.jacr.2020.08.016.
9
Noninterpretive Uses of Artificial Intelligence in Radiology.
Acad Radiol. 2021 Sep;28(9):1225-1235. doi: 10.1016/j.acra.2020.01.012. Epub 2020 Feb 12.
10
Strengths, Weaknesses, Opportunities, and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology.
J Am Coll Radiol. 2019 Sep;16(9 Pt B):1239-1247. doi: 10.1016/j.jacr.2019.05.047.
Zotero 插件