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从长椅到床边用大型语言模型: 专家小组叙述审查:
Rajesh Bhayana1,2, Som Biswas3, Tessa S Cook4
1University Medical Imaging Toronto, Joint Department of Medical Imaging, University Health Network, Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.
AJR. American journal of roentgenology
|April 10, 2024
概括
大型语言模型 (LLM) 为放射学提供了巨大的潜力,但需要仔细实施. 放射科医生必须指导这些人工智能 (AI) 工具的整合,以确保患者的安全并提高护理质量.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 大型语言模型 (LLM) 对放射学领域具有变革潜力.
- 目前的通用LLM和AI聊天机器人面临隐私,透明度和准确性方面的挑战,阻碍了临床采用.
- 对放射学LLM特定工具的开发和验证仍处于早期阶段,尽管有新兴的商业产品.
研究的目的:
- 为了提供一个全面的,多维的LLMs在放射学的审查,从开发到临床应用.
- 引导放射科医生在不断变化的LLM整合领域进行导航.
- 突出负责实施的考虑因素,以最大限度地提高收益并减轻风险.
主要方法:
- 叙事审查方法.
- 专家小组的见解. 专家小组的见解.
- 多维视角,包括板和床边考虑.
主要成果:
- 目前LLM无法自主决策;放射科医生保留了报告内容的责任.
- 面向患者的AI工具,如医疗聊天机器人,需要强有力的安全措施和监督.
- 负责任地实施LLMs可以显著提高放射学实践的效率和质量.
结论:
- 如果负责任地实施,LLM有望提高放射学效率和质量.
- 放射科医生必须积极参与指导LLMs的整合,以确保患者的安全和最佳结果.
- 进一步的研究和验证对于临床放射学LLM应用至关重要.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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