Background This systematic review aims to comprehensively survey digital technologies used in the prevention, diagnosis, and treatment of hereditary blood coagulation disorders.

Methods The systematic review was performed according to the PRISMA guidelines. A systematic search was conducted on PubMed on January 29, 2024. Articles were excluded if they were reviews, meta-analyses, or systematic reviews. Articles were included if they were published from January 1, 2014, onward, written in English, described an actual application of digital tools, were in the context of hereditary coagulation disorders, and involved studies or trials on humans or human data with at least three subjects.

Results The initial PubMed search on January 29, 2024, identified 2,843 articles, with 672 from January 1, 2014, onward. After screening, 21 articles met the exclusion and inclusion criteria. Among these, 12 focused on artificial intelligence (AI) technologies and 9 on digital applications. AI was predominantly used for diagnosis (five studies) and treatment (four studies), while digital applications were mainly used for treatment (eight studies). Most studies addressed hemophilia A, with a smaller number including hemophilia B or von Willebrand disease.

Discussion The findings reveal a lack of intervention studies in the prevention, diagnosis, and treatment. However, digital tools, including AI and digital applications, are increasingly used in managing hereditary coagulation disorders. AI enhances diagnostic accuracy and personalizes treatment, while digital applications improve patient care and engagement. Despite these advancements, study biases and design limitations indicate the need for further research to fully harness the potential of these technologies.

F.K., M.K., and S.M.J. conceived and designed the analysis. M.K. extracted titles and abstracts from PubMed and applied the automatic filtering by date range and language. All authors equally participated in the double-checked screening of titles and abstracts for inclusion. F.K., M.K., and L.N. handled conflicts in titles and abstract inclusion. During the full-text screening, F.K. reviewed all the articles, while the other authors screened the articles by equally sharing among them, ensuring double-checking. Information extraction from the full articles was performed, with F.K. handling all articles and the remaining workload was equally shared among the others, and also double-checked. F.K. wrote the manuscript, which was critically revised by all authors. They provided final approval of the version to be published and agreed to be accountable for all aspects of the work.

During the preparation of this work, we used generative AI to proofread the text and eliminate typos and grammatical flaws. After that, the authors reviewed and edited the content as needed and took full responsibility for the publication's content.

Received: 01 August 2024

Accepted: 14 September 2024

Article published online:
10 December 2024

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