[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127702-en":3,"doc-seo-127702-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127702,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Are the European reference networks for rare diseases ready to embrace machine learning - A mixed-methods study","Rare disease patients often experience diagnosis delays that are longer than those seen in common conditions. Machine learning (ML) may accelerate diagnosis while improving precision in this population. The study explores European Reference Networks (ERNs) members’ expectations and experiences with ML and considers how these technologies might be applied in practice. A mixed-methods design combined an online survey with focus group discussion among professionals affiliated with the 24 ERNs.","Iskrov et al. Orphanet Journal of Rare Diseases (2024) 19:25  \n[https://doi.org/10.1186/s13023-024-03047-7](https://doi.org/10.1186/s13023-024-03047-7)  \nOrphanet Journal of Rare Diseases  \n RESEARCH Open Access  \nAre the European reference networks for rare   diseases ready to embrace machine learning? A mixed-methods study  \nGeorgi Iskrov 1,2* , Ralitsa Raycheva 1,2, Kostadin Kostadinov 1,2, Sandra Gillner3,4, Carl Rudolf Blankart3,4, Edith Sky Gross5, Gulcin Gumus5, Elena Mitova 1, Stefan Stefanov1,6, Georgi Stefanov1 and Rumen Stefanov1,2  \nAbstract  \nBackground 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 ofthe members of the European Reference Networks (ERNs) for RDs with those technologies and their potential for application.  \nMethods 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.  \nResults 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.  \nKeywords Rare diseases, Machine learning, Artificial intelligence, European reference networks, Diagnosis, Diagnostic delay  \n*Correspondence:  \nGeorgi Iskrov  \n[georgi.g.iskrov@gmail.com](georgi.g.iskrov@gmail.com)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this","cbCail6gf02JSTgn","https://ap.wps.com/l/cbCail6gf02JSTgn","pdf",1503594,1,19,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusions","[{\"question\":\"What motivated this study on machine learning for rare disease diagnosis?\",\"answer\":\"Rare disease patients often face long diagnostic delays. The study is motivated by ML’s potential to speed up and increase the precision of diagnosis for this group.\"},{\"question\":\"How was the research conducted?\",\"answer\":\"The researchers used a mixed-methods approach: an online survey followed by focus group discussions. The study targeted medical professionals and other individuals affiliated with the 24 European Reference Networks.\"},{\"question\":\"What did participants identify as the biggest potential benefits and barriers?\",\"answer\":\"Participants reported limited knowledge and experience with ML. They highlighted improved diagnostic accuracy as the top potential benefit, while lack of training was viewed as a key barrier to adoption in routine care.\"}]","Are the European reference networks for rare diseases ready to embrace machine learning - A mixed-methods study | PDF",1785941010,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"are-the-european-reference-networks-for-rare-diseases-ready-to-embrace-machine-learning-a-mixed-methods-study","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/are-the-european-reference-networks-for-rare-diseases-ready-to-embrace-machine-learning-a-mixed-methods-study/127702/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What motivated this study on machine learning for rare disease diagnosis?","Question",{"text":75,"@type":76},"Rare disease patients often face long diagnostic delays. The study is motivated by ML’s potential to speed up and increase the precision of diagnosis for this group.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the research conducted?",{"text":80,"@type":76},"The researchers used a mixed-methods approach: an online survey followed by focus group discussions. The study targeted medical professionals and other individuals affiliated with the 24 European Reference Networks.",{"name":82,"@type":73,"acceptedAnswer":83},"What did participants identify as the biggest potential benefits and barriers?",{"text":84,"@type":76},"Participants reported limited knowledge and experience with ML. 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