分析人工智能创新在放射学实践中被接受的障碍和推动因素:一个范围审查
Fatma A Eltawil1, Michael Atalla1, Emily Boulos1
1Department of Medical Imaging, University of Toronto, Toronto, ON M5S 1A1, Canada.
Tomography (Ann Arbor, Mich.)
|August 25, 2023
概括
放射科医生 放射科医生 放射科医生
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 人工智能/机器学习 (AI/ML) 越来越多地融入医疗实践.
- 了解医生的观点对于成功地在放射学中采用人工智能至关重要.
- 现有的研究往往侧重于人工智能取代放射科医生,而不是更广泛的采用障碍.
研究的目的:
- 从医生的角度来看,确定AI/ML创新在放射学中的接受障碍和促进因素.
- 综合当前关于影响人工智能在放射实践中的整合因素的证据.
主要方法:
- 2000年1月至2023年间发表的研究的系统范围审查.
- 在Ovid Medline和Embase中使用与人工智能,计算机辅助诊断和采用障碍/支持者相关的关键词进行搜索.
- 包括定性研究,调查研究和随机对照试验 (RCT),严格的审查者评估和偏见缓解.
主要成果:
- 主要障碍包括放射科医生缺乏信任和接受,知识不足以及对专业自主权的威胁.
- 显著的推动因素是人工智能的预期附加值,减少诊断错误的可能性,提高效率和改善患者护理质量.
- 十二项研究符合纳入标准,突出了需要更有针对性的研究.
结论:
- 很少有研究专门讨论人工智能在放射学实践中的接受障碍和支持因素.
- 进一步的强有力的研究对于了解利益相关者的态度和全面评估人工智能集成的好处和缺点至关重要.
- 解决医生的担忧,强调人工智能的支持作用是成功采用的关键.
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