Are the European reference networks for rare diseases ready to embrace machine learning? A mixed-methods study

Are the European reference networks for rare diseases ready to embrace machine learning? A mixed-methods study

Abstract Background The delay in diagnosis for rare disease (RD) patients is often longer than for patients with common diseases. Machine learning (ML) technologies have the potential to speed up and increase the precision of diagnosis in this population group. We aim to explore the expectations and...

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Bibliographic Details
Main Authors: Georgi Iskrov, Ralitsa Raycheva, Kostadin Kostadinov, Sandra Gillner, Carl Rudolf Blankart, Edith Sky Gross, Gulcin Gumus, Elena Mitova, Stefan Stefanov, Georgi Stefanov, Rumen Stefanov
Format: Article
Language:English
Published: BMC 2024-01-01
Series:Orphanet Journal of Rare Diseases
Subjects:
Rare diseases
Machine learning
Artificial intelligence
European reference networks
Diagnosis
Diagnostic delay
Online Access:https://doi.org/10.1186/s13023-024-03047-7

Internet

https://doi.org/10.1186/s13023-024-03047-7

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