作为医生的放射科医生--人工智能是克服病人、技术和转诊医生之间紧张关系的一种方法--叙述性评论。

IF 1.3 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Rofo-fortschritte Auf Dem Gebiet Der Rontgenstrahlen Und Der Bildgebenden Verfahren Pub Date : 2024-11-01 Epub Date: 2024-04-03 DOI:10.1055/a-2271-0799
Christoph Alexander Stueckle, Patrick Haage
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This \"explainability\" creates trust in an AI system and shows its limits.</p><p><strong>Method: </strong> Based on an expanded Medline search for the key words \"radiology, artificial intelligence, referring physician interaction, patient interaction, job satisfaction, communication of findings, expectations\", subjective additional relevant articles were considered for this narrative review.</p><p><strong>Results: </strong> The use of AI is well advanced, especially in radiology. The programmer should provide the radiologist with clear explanations as to how the system works. All systems on the market have strengths and weaknesses. Some of the optimizations are unintentionally specific, as they are often adapted too precisely to a certain environment that often does not exist in practice - this is known as \"overfitting\". 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引用次数: 0

摘要

背景:随着时间的推移,大量数据不断增加,导致放射科医生时间不足。使用基于人工智能(AI)的系统为减轻放射科医生的负担提供了机会。人工智能系统通常针对放射学领域进行优化。放射科医生必须了解其技术功能的基本特征,以便能够评估系统的弱点和可能出现的错误,并利用系统的优势。这种 "可解释性 "使人们对人工智能系统产生信任,并显示出其局限性:方法:以 "放射学、人工智能、转诊医生互动、患者互动、工作满意度、检查结果沟通、期望值 "为关键词,在Medline上进行扩展搜索,并在此基础上考虑了其他相关文章:人工智能的应用非常先进,尤其是在放射学领域。程序员应向放射科医生清楚解释系统的工作原理。市场上的所有系统都有优缺点。有些优化是无意的,因为它们往往过于精确地适应某种环境,而这种环境在实践中往往并不存在,这就是所谓的 "过度拟合"。还应该指出的是,系统中存在一些特定的弱点,即所谓的 "对抗范例",它们会导致人工智能出现致命的误诊,尽管放射科医生无法直观地将这些误诊与不值得注意的发现区分开来。用户必须知道系统针对哪些疾病进行了训练,人工智能识别并考虑了哪些器官系统,以及哪些器官系统没有得到正确评估。这意味着用户可以而且必须严格审查结果,并在必要时调整检查结果。正确应用人工智能可以为放射科医生节省时间。如果他知道系统是如何工作的,他只需花很短的时间检查结果。节省下来的时间可以用来与病人和转诊医生沟通,从而提高工作满意度:放射学是一门不断发展的专业,责任重大,因为放射科医生经常做出需要治疗的诊断。应持续使用人工智能支持系统来提供帮助和支持。放射科医生需要了解这些人工智能系统的优缺点和应用领域,以节省时间。节省下来的时间可用于与患者和转诊医生沟通:- 可解释的人工智能系统有助于改进工作流程并节省时间。- 考虑到人工智能的局限性,医生必须严格审查人工智能的结果。- 人工智能系统只有适应数据类型和数据来源,才能提供有用的结果。- 对患者感兴趣的放射科医生的沟通对于学科的可见性非常重要:- Stueckle CA, Haage P.作为医生的放射科医生--人工智能是克服患者、技术和转诊医生之间紧张关系的途径--叙事性综述。Fortschr Röntgenstr 2024; DOI: 10.1055/a-2271-0799.
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The radiologist as a physician - artificial intelligence as a way to overcome tension between the patient, technology, and referring physicians - a narrative review.

Background:  Large volumes of data increasing over time lead to a shortage of radiologists' time. The use of systems based on artificial intelligence (AI) offers opportunities to relieve the burden on radiologists. The AI systems are usually optimized for a radiological area. Radiologists must understand the basic features of its technical function in order to be able to assess the weaknesses and possible errors of the system and use the strengths of the system. This "explainability" creates trust in an AI system and shows its limits.

Method:  Based on an expanded Medline search for the key words "radiology, artificial intelligence, referring physician interaction, patient interaction, job satisfaction, communication of findings, expectations", subjective additional relevant articles were considered for this narrative review.

Results:  The use of AI is well advanced, especially in radiology. The programmer should provide the radiologist with clear explanations as to how the system works. All systems on the market have strengths and weaknesses. Some of the optimizations are unintentionally specific, as they are often adapted too precisely to a certain environment that often does not exist in practice - this is known as "overfitting". It should also be noted that there are specific weak points in the systems, so-called "adversarial examples", which lead to fatal misdiagnoses by the AI even though these cannot be visually distinguished from an unremarkable finding by the radiologist. The user must know which diseases the system is trained for, which organ systems are recognized and taken into account by the AI, and, accordingly, which are not properly assessed. This means that the user can and must critically review the results and adjust the findings if necessary. Correctly applied AI can result in a time savings for the radiologist. If he knows how the system works, he only has to spend a short amount of time checking the results. The time saved can be used for communication with patients and referring physicians and thus contribute to higher job satisfaction.

Conclusion:  Radiology is a constantly evolving specialty with enormous responsibility, as radiologists often make the diagnosis to be treated. AI-supported systems should be used consistently to provide relief and support. Radiologists need to know the strengths, weaknesses, and areas of application of these AI systems in order to save time. The time gained can be used for communication with patients and referring physicians.

Key points:   · Explainable AI systems help to improve workflow and to save time.. · The physician must critically review AI results, under consideration of the limitations of the AI.. · The AI system will only provide useful results if it has been adapted to the data type and data origin.. · The communicating radiologist interested in the patient is important for the visibility of the discipline..

Citation format: · Stueckle CA, Haage P. The radiologist as a physician - artificial intelligence as a way to overcome tension between the patient, technology, and referring physicians - a narrative review. Fortschr Röntgenstr 2024; 196: 1115 - 1123.

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