[人工智能诊断的捷径:关于诊断决策支持系统的系统文献综述]。

IF 1.1 4区 医学 Q3 ANESTHESIOLOGY
Schmerz Pub Date : 2024-02-01 Epub Date: 2024-01-02 DOI:10.1007/s00482-023-00777-8
Julia Sellin, Jean Tori Pantel, Natalie Börsch, Rupert Conrad, Martin Mücke
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引用次数: 0

摘要

背景:罕见疾病往往很晚才被发现。由于临床症状的多样性、复杂性和异质性,这些疾病的诊断尤其具有挑战性。计算机辅助诊断辅助工具,通常被称为诊断决策支持系统(DDSS),是缩短诊断时间的有效工具。尽管诊断决策支持系统得到了初步的积极评价,但尚未得到广泛应用,部分原因是该系统与现有的临床或实践信息系统缺乏整合:这篇文章对目前现有的诊断支持系统进行了深入分析,这些系统无需访问电子病历,只需要容易获取的信息即可运行:通过系统性文献检索,我们找到了 8 篇关于 DDSS 的文章,这些系统可协助诊断罕见病,且无需访问电子病历或诊疗机构和医院的其他信息系统。对已发现的罕见病诊断支持系统的主要优缺点进行了提取和总结:结果:基于肖像照片和疼痛图纸的症状检查器和罕见疾病诊断支持系统已经存在。这些应用的成熟程度各不相同:目前,DDSS 仍面临着许多挑战,如数据保护和准确性方面的担忧,接受度和认知度仍然较低。另一方面,它在加快诊断速度方面具有巨大潜力,尤其是对罕见疾病的诊断。因此,医生应根据具体情况慎重考虑是否使用数据收集和分析系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

[Short paths to diagnosis with artificial intelligence: systematic literature review on diagnostic decision support systems].

[Short paths to diagnosis with artificial intelligence: systematic literature review on diagnostic decision support systems].

Background: Rare diseases are often recognized late. Their diagnosis is particularly challenging due to the diversity, complexity and heterogeneity of clinical symptoms. Computer-aided diagnostic aids, often referred to as diagnostic decision support systems (DDSS), are promising tools for shortening the time to diagnosis. Despite initial positive evaluations, DDSS are not yet widely used, partly due to a lack of integration with existing clinical or practice information systems.

Objective: This article provides an insight into currently existing diagnostic support systems that function without access to electronic patient records and only require information that is easily obtainable.

Materials and methods: A systematic literature search identified eight articles on DDSS that can assist in the diagnosis of rare diseases with no need for access to electronic patient records or other information systems in practices and hospitals. The main advantages and disadvantages of the identified rare disease diagnostic support systems were extracted and summarized.

Results: Symptom checkers and DDSS based on portrait photos and pain drawings already exist. The degree of maturity of these applications varies.

Conclusion: DDSS currently still face a number of challenges, such as concerns about data protection and accuracy, and acceptance and awareness continue to be rather low. On the other hand, there is great potential for faster diagnosis, especially for rare diseases, which are easily overlooked due to their large number and the low awareness of them. The use of DDSS should therefore be carefully considered by doctors on a case-by-case basis.

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来源期刊
Schmerz
Schmerz 医学-临床神经学
CiteScore
2.00
自引率
20.00%
发文量
64
审稿时长
6-12 weeks
期刊介绍: Der Schmerz is an internationally recognized journal and addresses all scientists, practitioners and psychologists, dealing with the treatment of pain patients or working in pain research. The aim of the journal is to enhance the treatment of pain patients in the long run. Review articles provide an overview on selected topics and offer the reader a summary of current findings from all fields of pain research, pain management and pain symptom management. Freely submitted original papers allow the presentation of important clinical studies and serve the scientific exchange. Case reports feature interesting cases and aim at optimizing diagnostic and therapeutic strategies. Review articles under the rubric ''Continuing Medical Education'' present verified results of scientific research and their integration into daily practice.
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