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. 2024 Jan 25;19(1):25.
doi: 10.1186/s13023-024-03047-7.

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

Georgi Iskrov  1   2 , Ralitsa Raycheva  3   4 , Kostadin Kostadinov  3   4 , Sandra Gillner  5   6 , Carl Rudolf Blankart  5   6 , Edith Sky Gross  7 , Gulcin Gumus  7 , Elena Mitova  3 , Stefan Stefanov  3   8 , Georgi Stefanov  3 , Rumen Stefanov  3   4
Affiliations

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

Georgi Iskrov et al. Orphanet J Rare Dis. .

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 experiences of the members of the European Reference Networks (ERNs) for RDs with those technologies and their potential for application.

Methods: We used a mixed-methods approach with an online survey followed by a focus group discussion. Our study targeted primarily medical professionals but also other individuals affiliated with any of the 24 ERNs.

Results: The online survey yielded 423 responses from ERN members. Participants reported a limited degree of knowledge of and experience with ML technologies. They considered improved diagnostic accuracy the most important potential benefit, closely followed by the synthesis of clinical information, and indicated the lack of training in these new technologies, which hinders adoption and implementation in routine care. Most respondents supported the option that ML should be an optional but recommended part of the diagnostic process for RDs. Most ERN members saw the use of ML limited to specialised units only in the next 5 years, where those technologies should be funded by public sources. Focus group discussions concluded that the potential of ML technologies is substantial and confirmed that the technologies will have an important impact on healthcare and RDs in particular. As ML technologies are not the core competency of health care professionals, participants deemed a close collaboration with developers necessary to ensure that results are valid and reliable. However, based on our results, we call for more research to understand other stakeholders' opinions and expectations, including the views of patient organisations.

Conclusions: We found enthusiasm to implement and apply ML technologies, especially diagnostic tools in the field of RDs, despite the perceived lack of experience. Early dialogue and collaboration between health care professionals, developers, industry, policymakers, and patient associations seem to be crucial to building trust, improving performance, and ultimately increasing the willingness to accept diagnostics based on ML technologies.

Keywords: Artificial intelligence; Diagnosis; Diagnostic delay; European reference networks; Machine learning; Rare diseases.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Distribution of respondents by self-assessed knowledge of ML on a 1–5 scale
Fig. 2
Fig. 2
Distribution of respondents by self-reported ML experience
Fig. 3
Fig. 3
Assessment of ML potential benefits on a 1–5 scale
Fig. 4
Fig. 4
Assessment of ML potential risks on a 1–5 scale
Fig. 5
Fig. 5
Distribution of respondents by preferred scope and type of ML diagnostic findings to be disclosed to patients
Fig. 6
Fig. 6
Distribution of respondents by preferred funding of ML diagnostics
Fig. 7
Fig. 7
Distribution of respondents by preferred secondary use of ML anonymous data for research
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References

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