The application of artificial intelligence in the sonography profession: Professional and educational considerations

Author:

Edwards Christopher12ORCID,Chamunyonga Crispen132,Searle Benjamin13ORCID,Reddan Tristan14

Affiliation:

1. School of Clinical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia

2. Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia

3. Department of Medical Imaging, Redcliffe Hospital, Redcliffe, QLD, Australia

4. Medical Imaging and Nuclear Medicine, Queensland Children’s Hospital, South Brisbane, QLD, Australia

Abstract

The integration of artificial intelligence (AI) technology within the health industry is increasing. This educational piece discusses the implementation of AI and its impact on sonography. The authors investigate how AI may influence the profession and provide examples of how ultrasound imaging may be enhanced and innovated by integrating AI technology. This article highlights challenges related to the application of AI and provides insight into how they could be addressed. The critical distinction between the role of a sonographer and the reporting specialist in the context of AI is highlighted as a key issue for those developing, researching, and evaluating AI systems. A key recommendation is for the sonography community to address ultrasound education, particularly how AI knowledge could be incorporated into university education. This is an important consideration that should be extended to practising professionals as they may be involved in evaluating the efficiency and methodologies used in new research that may incorporate AI technologies.

Publisher

SAGE Publications

Subject

Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference57 articles.

1. Artificial intelligence in medicine: current trends and future possibilities

2. Artificial Intelligence in Health Care: Brave New World or Golden Opportunity?

3. Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success

4. PubMed, https://pubmed.ncbi.nlm.nih.gov/ (accessed 29 July 2021).

5. American College of Radiology Data Science Institute® (DSI), https://www.acrdsi.org/ (accessed 30 July 2021).

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3