Literature DB >> 30470627

Artificial intelligence and medical imaging 2018: French Radiology Community white paper.

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Abstract

The rapid development of information technology and data processing capabilities has led to the creation of new tools known as artificial intelligence (AI). Medical applications of AI are emerging, and the French radiology community felt it was therefore timely to issue a position paper on AI as part of its role as a leader in the development of digital projects. Essential information about the application of AI to radiology includes a description of the available algorithms with a glossary; a review of the issues raised by healthcare data, notably those pertaining to imaging (imaging data and co-variables, metadata); a look at research and innovation; an overview of current and future applications; a discussion of AI education; and a scrutiny of ethical issues. In addition to the principles set forth at the Asilomar Conference on Beneficial AI, the French radiology community has developed ten principles aimed at governing the use and development of AI tools in a manner that will create a concerted approach centered on benefits to patients, while also ensuring good integration within clinical workflows. High-quality care in radiology and opportunities for managing large datasets are two avenues relevant to the development of a precision, personalized, and participative radiology practice characterized by improved predictive and preventive capabilities.
Copyright © 2018 Soci showét showé françaises de radiologie. Published by Elsevier Masson SAS. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; Principle-based ethics; Radiology; Technology

Mesh:

Year:  2018        PMID: 30470627     DOI: 10.1016/j.diii.2018.10.003

Source DB:  PubMed          Journal:  Diagn Interv Imaging        ISSN: 2211-5684            Impact factor:   4.026


  17 in total

1.  Identifying Ethical Considerations for Machine Learning Healthcare Applications.

Authors:  Danton S Char; Michael D Abràmoff; Chris Feudtner
Journal:  Am J Bioeth       Date:  2020-11       Impact factor: 11.229

2.  AI Techniques for COVID-19.

Authors:  Adedoyin Ahmed Hussain; Ouns Bouachir; Fadi Al-Turjman; Moayad Aloqaily
Journal:  IEEE Access       Date:  2020-07-08       Impact factor: 3.367

Review 3.  Artificial Intelligence: A Primer for Breast Imaging Radiologists.

Authors:  Manisha Bahl
Journal:  J Breast Imaging       Date:  2020-06-19

4.  Opportunities and challenges of artificial intelligence in the medical field: current application, emerging problems, and problem-solving strategies.

Authors:  Lushun Jiang; Zhe Wu; Xiaolan Xu; Yaqiong Zhan; Xuehang Jin; Li Wang; Yunqing Qiu
Journal:  J Int Med Res       Date:  2021-03       Impact factor: 1.671

5.  Attitude of Brazilian dentists and dental students regarding the future role of artificial intelligence in oral radiology: a multicenter survey.

Authors:  Ruben Pauwels; Yumi Chokyu Del Rey
Journal:  Dentomaxillofac Radiol       Date:  2021-01-12       Impact factor: 3.525

6.  Artificial Intelligence in Radiology-Ethical Considerations.

Authors:  Adrian P Brady; Emanuele Neri
Journal:  Diagnostics (Basel)       Date:  2020-04-17

Review 7.  Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review.

Authors:  A Hasan Sapci; H Aylin Sapci
Journal:  JMIR Med Educ       Date:  2020-06-30

8.  Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers.

Authors:  Dong Wook Kim; Hye Young Jang; Kyung Won Kim; Youngbin Shin; Seong Ho Park
Journal:  Korean J Radiol       Date:  2019-03       Impact factor: 3.500

Review 9.  Artificial Intelligence: Practical Primer for Clinical Research in Cardiovascular Disease.

Authors:  Nobuyuki Kagiyama; Sirish Shrestha; Peter D Farjo; Partho P Sengupta
Journal:  J Am Heart Assoc       Date:  2019-08-27       Impact factor: 5.501

Review 10.  Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease.

Authors:  Yinhe Feng; Yubin Wang; Chunfang Zeng; Hui Mao
Journal:  Int J Med Sci       Date:  2021-06-01       Impact factor: 3.738

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