Skip to main content

Advertisement

Log in

Integrating blockchain technology with artificial intelligence for cardiovascular medicine

  • Comment
  • Published:

From Nature Reviews Cardiology

View current issue Sign up to alerts

Artificial intelligence (AI) holds promise for cardiovascular medicine but is limited by a lack of large, heterogeneous and granular data sets. Blockchain provides secure interoperability between siloed stakeholders and centralized data sources. We discuss integration of blockchain with AI for data-centric analysis and information flow, its current limitations and potential cardiovascular applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1: Integration of AI with blockchain moves patients towards the centre of the health-care process.

References

  1. Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019).

    Article  CAS  Google Scholar 

  2. Krittanawong, C. et al. Deep learning for cardiovascular medicine: a practical primer. Eur. Heart J. 40, 2058–2073 (2019).

    Article  Google Scholar 

  3. Minchole, A. & Rodriguez, B. Artificial intelligence for the electrocardiogram. Nat. Med. 25, 22–23 (2019).

    Article  CAS  Google Scholar 

  4. Loring, Z., Mehrotra, S. & Piccini, J. P. Machine learning in ‘big data’: handle with care. Europace 21, 1284–1285 (2019).

    Article  Google Scholar 

  5. Giordanengoa, A. Possible usages of smart contracts (blockchain) in healthcare and why no one is using them. Stud. Health Technol. Inform. 264, 596–600 (2019).

    Google Scholar 

  6. Mamoshina, P. et al. Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare. Oncotarget 9, 5665–5690 (2018).

    Article  Google Scholar 

  7. de Denus, S. et al. Spironolactone metabolites in TOPCAT — new insights into regional variation. N. Engl. J. Med. 376, 1690–1692 (2017).

    Article  Google Scholar 

  8. Wiggers, K. PatientSphere uses AI and blockchain to personalize treatment plans. VentureBeat https://venturebeat.com/2018/10/25/patientsphere-uses-ai-and-blockchain-to-personalize-treatment-plans/ (2018).

  9. Popov, G. The future of artifical intelligence in healthcare! SkyChain https://skychain.global/upload/iblock/89a/wp_english_Newest.pdf (2019).

  10. O’Donoghue, O. et al. Design choices and trade-offs in health care blockchain implementations: systematic review. J. Med. Internet Res. 21, e12426 (2019).

    Article  Google Scholar 

Download references

Acknowledgements

The NIH has awarded grant funding to A.J.R. (F32HL144101) and S.M.N. (HL83359 and HL103800).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Chayakrit Krittanawong or Sanjiv M. Narayan.

Ethics declarations

Competing interests

S.M.N. has consulted for Abbott Laboratories and beyond.ai and declares Intellectual Property Rights from University of California Regents and Stanford University. The other authors declare no competing interests.

Additional information

RELATED LINKS

Bitcoin: https://bitcoin.org/en/

Ethereum: https://www.ethereum.org/

Farasha Labs: https://www.f6s.com/farashalabs

Health2Sync: https://www.health2sync.com/

MedStar Health Research Institute: https://www.medstarhealth.org/mhri/

ObEN: https://oben.me/

Open Health Network: https://www.openhealth.cc/

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Krittanawong, C., Rogers, A.J., Aydar, M. et al. Integrating blockchain technology with artificial intelligence for cardiovascular medicine. Nat Rev Cardiol 17, 1–3 (2020). https://doi.org/10.1038/s41569-019-0294-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41569-019-0294-y

  • Springer Nature Limited

This article is cited by

Navigation