• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

[利用人工智能更快地诊断罕见病——伦理、经济与生活质量的准则]

[Faster diagnosis of rare diseases with artificial intelligence-A precept of ethics, economy and quality of life].

作者信息

Völkel Lukas, Wagner Annette D

机构信息

Medizinische Hochschule Hannover, Carl-Neuberg-Str. 1, 30625, Hannover, Deutschland.

Abteilung für Nieren- und Hochdruckerkrankungen, Ambulanz für seltene entzündliche Systemerkrankungen mit Nierenbeteiligung, Medizinische Hochschule Hannover, Carl-Neuberg-Str. 1, 30625, Hannover, Deutschland.

出版信息

Inn Med (Heidelb). 2023 Nov;64(11):1033-1040. doi: 10.1007/s00108-023-01599-7. Epub 2023 Oct 20.

DOI:10.1007/s00108-023-01599-7
PMID:37861723
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10602953/
Abstract

BACKGROUND

Approximately 300 million people worldwide suffer from a rare disease. An optimal treatment requires a successful diagnosis. This takes a particularly long time, especially for rare diseases. Digital diagnosis support systems could be important aids in accelerating a successful diagnosis in the future.

OBJECTIVE

The current possibilities of digital diagnostic support systems in the diagnosis of rare diseases and questions that still need to be clarified are presented in relation to the parameters of ethics, economy and quality of life.

MATERIAL AND METHODS

Current research results of the authors were compiled and discussed in the context of the current literature. A case study is used to illustrate the potential of digital diagnostic support systems.

RESULTS

Digital diagnostic support systems and experts together can accelerate the successful diagnosis in patients with rare diseases. This could have a positive impact on patients' quality of life and lead to potential savings in direct and indirect costs in the healthcare system.

CONCLUSION

Ensuring data security, legal certainty and functionality in the use of digital diagnostic support systems is of great importance in order to create trust among experts and patients. Continuous further development of the systems by means of artificial intelligence (AI) could also enable patients to accelerate diagnosis in the future.

摘要

背景

全球约有3亿人患有罕见病。最佳治疗需要成功诊断。这尤其耗时，特别是对于罕见病而言。数字诊断支持系统可能是未来加速成功诊断的重要辅助手段。

目的

结合伦理、经济和生活质量参数，介绍数字诊断支持系统在罕见病诊断中的当前可能性以及仍需澄清的问题。

材料与方法

整理作者当前的研究结果，并结合当前文献进行讨论。通过一个案例研究来说明数字诊断支持系统的潜力。

结果

数字诊断支持系统与专家共同作用可加速罕见病患者的成功诊断。这可能对患者的生活质量产生积极影响，并在医疗系统中带来直接和间接成本的潜在节省。

结论

在使用数字诊断支持系统时确保数据安全、法律确定性和功能，对于在专家和患者之间建立信任非常重要。通过人工智能（AI）持续进一步开发这些系统，未来也可能使患者加速诊断。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f11/10602953/2684c0e92e4d/108_2023_1599_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f11/10602953/3a68c063c8a3/108_2023_1599_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f11/10602953/688921a2f566/108_2023_1599_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f11/10602953/2684c0e92e4d/108_2023_1599_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f11/10602953/3a68c063c8a3/108_2023_1599_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f11/10602953/688921a2f566/108_2023_1599_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f11/10602953/2684c0e92e4d/108_2023_1599_Fig3_HTML.jpg

相似文献

1
[Faster diagnosis of rare diseases with artificial intelligence-A precept of ethics, economy and quality of life].[利用人工智能更快地诊断罕见病——伦理、经济与生活质量的准则]
Inn Med (Heidelb). 2023 Nov;64(11):1033-1040. doi: 10.1007/s00108-023-01599-7. Epub 2023 Oct 20.
2
[Interdisciplinary care path and potential IT support for people with rare diseases in Germany].[德国罕见病患者的跨学科护理路径及潜在的信息技术支持]
Z Evid Fortbild Qual Gesundhwes. 2021 Oct;165:68-76. doi: 10.1016/j.zefq.2021.06.004. Epub 2021 Sep 3.
3
New and emerging technology for adult social care - the example of home sensors with artificial intelligence (AI) technology.成人社会关怀新技术——以具有人工智能 (AI) 技术的家庭传感器为例。
Health Soc Care Deliv Res. 2023 Jun;11(9):1-64. doi: 10.3310/HRYW4281.
4
[The transformative effect of artificial intelligence in hospitals : The focus is on the individual].[人工智能在医院中的变革性影响：聚焦个体]
Inn Med (Heidelb). 2023 Nov;64(11):1025-1032. doi: 10.1007/s00108-023-01597-9. Epub 2023 Oct 18.
5
[Short paths to diagnosis with artificial intelligence: systematic literature review on diagnostic decision support systems].[借助人工智能实现快速诊断：关于诊断决策支持系统的系统文献综述]
Schmerz. 2024 Feb;38(1):19-27. doi: 10.1007/s00482-023-00777-8. Epub 2024 Jan 2.
6
Health economic benefits through the use of diagnostic support systems and expert knowledge.通过使用诊断支持系统和专家知识获得健康经济效益。
BMC Health Serv Res. 2021 Sep 9;21(1):947. doi: 10.1186/s12913-021-06926-y.
7
Artificial intelligence for good health: a scoping review of the ethics literature.促进健康的人工智能：伦理文献的范围综述
BMC Med Ethics. 2021 Feb 15;22(1):14. doi: 10.1186/s12910-021-00577-8.
8
The future of Cochrane Neonatal.考克兰新生儿协作网的未来。
Early Hum Dev. 2020 Nov;150:105191. doi: 10.1016/j.earlhumdev.2020.105191. Epub 2020 Sep 12.
9
Challenges in mapping European rare disease databases, relevant for ML-based screening technologies in terms of organizational, FAIR and legal principles: scoping review.绘制欧洲罕见病数据库图谱所面临的挑战，从组织、FAIR 和法律原则方面来看，这些挑战与基于机器学习的筛查技术相关：范围综述。
Front Public Health. 2023 Sep 15;11:1214766. doi: 10.3389/fpubh.2023.1214766. eCollection 2023.
10
"I don't think people are ready to trust these algorithms at face value": trust and the use of machine learning algorithms in the diagnosis of rare disease.“我认为人们还没有准备好完全信任这些算法”：信任和机器学习算法在罕见病诊断中的应用。
BMC Med Ethics. 2022 Nov 16;23(1):112. doi: 10.1186/s12910-022-00842-4.

引用本文的文献

1
Artificial Intelligence: A New Frontier in Rare Disease Early Diagnosis.人工智能：罕见病早期诊断的新前沿。
Cureus. 2025 Feb 22;17(2):e79487. doi: 10.7759/cureus.79487. eCollection 2025 Feb.

本文引用的文献

1
"I don't think people are ready to trust these algorithms at face value": trust and the use of machine learning algorithms in the diagnosis of rare disease.“我认为人们还没有准备好完全信任这些算法”：信任和机器学习算法在罕见病诊断中的应用。
BMC Med Ethics. 2022 Nov 16;23(1):112. doi: 10.1186/s12910-022-00842-4.
2
(De)troubling transparency: artificial intelligence (AI) for clinical applications.令人不安的透明度：临床应用中的人工智能 (AI)。
Med Humanit. 2023 Mar;49(1):17-26. doi: 10.1136/medhum-2021-012318. Epub 2022 May 11.
3
Health economic benefits through the use of diagnostic support systems and expert knowledge.
通过使用诊断支持系统和专家知识获得健康经济效益。
BMC Health Serv Res. 2021 Sep 9;21(1):947. doi: 10.1186/s12913-021-06926-y.
4
Diagnosis support systems for rare diseases: a scoping review.罕见病诊断支持系统：范围综述。
Orphanet J Rare Dis. 2020 Apr 16;15(1):94. doi: 10.1186/s13023-020-01374-z.
5
Rare diseases 2030: how augmented AI will support diagnosis and treatment of rare diseases in the future.2030年的罕见病：增强人工智能将如何在未来支持罕见病的诊断和治疗
Ann Rheum Dis. 2020 Jun;79(6):740-743. doi: 10.1136/annrheumdis-2020-217125. Epub 2020 Mar 24.
6
Improving rare disease classification using imperfect knowledge graph.利用不完善的知识图谱提高罕见病分类。
BMC Med Inform Decis Mak. 2019 Dec 5;19(Suppl 5):238. doi: 10.1186/s12911-019-0938-1.
7
Information needs of physicians regarding the diagnosis of rare diseases: a questionnaire-based study in Belgium.比利时一项基于问卷调查的研究：医生对罕见病诊断的信息需求。
Orphanet J Rare Dis. 2019 May 4;14(1):99. doi: 10.1186/s13023-019-1075-8.
8
Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study.决策支持系统能否加速罕见病诊断？一项回顾性研究评估 Ada DX 的潜在影响。
Orphanet J Rare Dis. 2019 Mar 21;14(1):69. doi: 10.1186/s13023-019-1040-6.
9
The burden of rare diseases.罕见病的负担。
Am J Med Genet A. 2019 Jun;179(6):885-892. doi: 10.1002/ajmg.a.61124. Epub 2019 Mar 18.
10
RDAD: A Machine Learning System to Support Phenotype-Based Rare Disease Diagnosis.RDAD：一种支持基于表型的罕见病诊断的机器学习系统。
Front Genet. 2018 Dec 4;9:587. doi: 10.3389/fgene.2018.00587. eCollection 2018.