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[Faster Diagnosis of Rare Diseases with Artificial Intelligence-A Precept of Ethics, Economy and Quality of Life]

Overview
Specialty General Medicine
Date 2023 Oct 20
PMID 37861723
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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.

References
1.
Faviez C, Chen X, Garcelon N, Neuraz A, Knebelmann B, Salomon R . Diagnosis support systems for rare diseases: a scoping review. Orphanet J Rare Dis. 2020; 15(1):94. PMC: 7164220. DOI: 10.1186/s13023-020-01374-z. View

2.
Li X, Wang Y, Wang D, Yuan W, Peng D, Mei Q . Improving rare disease classification using imperfect knowledge graph. BMC Med Inform Decis Mak. 2019; 19(Suppl 5):238. PMC: 6894101. DOI: 10.1186/s12911-019-0938-1. View

3.
Rother A, Schwerk N, Brinkmann F, Klawonn F, Lechner W, Grigull L . Diagnostic Support for Selected Paediatric Pulmonary Diseases Using Answer-Pattern Recognition in Questionnaires Based on Combined Data Mining Applications--A Monocentric Observational Pilot Study. PLoS One. 2015; 10(8):e0135180. PMC: 4534438. DOI: 10.1371/journal.pone.0135180. View

4.
Mueller T, Jerrentrup A, Bauer M, Fritsch H, Schaefer J . Characteristics of patients contacting a center for undiagnosed and rare diseases. Orphanet J Rare Dis. 2016; 11(1):81. PMC: 4915144. DOI: 10.1186/s13023-016-0467-2. View

5.
Bloss S, Klemann C, Rother A, Mehmecke S, Schumacher U, Mucke U . Diagnostic needs for rare diseases and shared prediagnostic phenomena: Results of a German-wide expert Delphi survey. PLoS One. 2017; 12(2):e0172532. PMC: 5325301. DOI: 10.1371/journal.pone.0172532. View