放射学基础模型:基础,应用,机遇,挑战,风险和前景
Tugba Akinci D'Antonoli1,2, Christian Bluethgen3, Renato Cuocolo4
1University Hospital Basel, Department of Diagnostic and Interventional Neuroradiology, Basel, Switzerland.
概括
基础模型 (FMs) 在放射学中提供先进的AI功能,提高了适应性和通用性. 挑战包括模型不透明,伦理,隐私和临床采用监管障碍.
科学领域:
- 人工智能 (AI) 是一种人工智能.
- 医疗成像医学成像
- 放射学 放射学是一门学科.
背景情况:
- 基础模型 (FMs) 与传统的狭窄AI相比,是一个显著的进步.
- FM提供了增强的适应性,多式联运一体化,以及在包括放射学在内的多个领域的概括性.
研究的目的:
- 为放射学中FM提供全面的概述.
- 讨论FM在临床实践中的应用,机遇和挑战.
- 引导在医疗保健中负责任地采用FM.
主要方法:
- 文献综述和综合目前在放射学中关于FM的研究.
- 分析FM的潜在应用,如图像解释和报告生成.
- 识别和讨论与临床翻译相关的挑战,包括道德和监管方面的问题.
主要成果:
- FM显示了各种应用的潜力,如集成诊断和合成数据创建.
- 主要挑战包括模型不透明性,数据隐私,道德考虑 (偏见,公平性) 和监管不确定性.
- 严格的验证对于解决随机性和潜在幻觉至关重要.
结论:
- 对于推进精准医学和医疗保健而言,FMS具有重大前景.
- 应对挑战对于放射学中FM的负责任和有效的临床转化至关重要.
- 本概述旨在促进有关FM采用的知情决策.
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