医学
叙述性评论
叙述的
限制
病人护理
质量(理念)
患者安全
医疗保健
透明度(行为)
从长凳到床边
医学物理学
计算机科学
重症监护医学
护理部
计算机安全
哲学
语言学
机械工程
认识论
工程类
经济
经济增长
作者
Rajesh Bhayana,Som Biswas,Tessa S. Cook,Woo Jin Kim,Felipe Kitamura,Judy Wawira Gichoya,Paul H. Yi
出处
期刊:American Journal of Roentgenology
[American Roentgen Ray Society]
日期:2024-04-10
被引量:1
摘要
Large language models (LLMs) hold immense potential to revolutionize radiology. However, their integration into practice requires careful consideration. Artificial intelligence (AI) chatbots and general-purpose LLMs have potential pitfalls related to privacy, transparency, and accuracy, limiting their current clinical readiness. Thus, LLM-based tools must be optimized for radiology practice to overcome these limitations. While research and validation for radiology applications remain in their infancy, commercial products incorporating LLMs are becoming available alongside promises of transforming practice. To help radiologists navigate this landscape, this AJR Expert Panel Narrative Review provides a multidimensional perspective on LLMs, encompassing considerations from bench (development and optimization) to bedside (use in practice). At present, LLMs are not autonomous entities that can replace expert decision-making, and radiologists remain responsible for the content of their reports. Patient-facing tools, particularly medical AI chatbots, require additional guardrails to ensure safety and prevent misuse. Still, if responsibly implemented, LLMs are well-positioned to transform efficiency and quality in radiology. Radiologists must be well-informed and proactively involved in guiding the implementation of LLMs in practice to mitigate risks and maximize benefits to patient care.
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