Opportunities and Risks of Technology Convergence in Precision Health

Authors

DOI:

https://doi.org/10.30953/bhty.v9.521

Keywords:

ConV2X, opportunities and risks, precision health, precision health outcomes, technology convergence

Abstract

This ConV2X Decentralized Health 2026 executive roundtable explores how technology convergence impacts precision health outcomes. The dialogue focuses on the main technologies supporting precision health, the latest scientific developments, technology advancements, opportunities, and risks. It is moderated by BHTY journal editor Prof. Dr. Vasiliu-Feltes. Participants emphasize the latest scientific developments, opportunities, and risks associated with precision health, enabled by converging advanced technologies.

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Author Biography

Ingrid Vasiliu-Feltes, MD, EMBA, FAPA, FACHE, Institute SEI, Herbert Business School, University of Miami, Miami, Florida, USA

This ConV2X Decentralized Health 2026 executive roundtable explores how technology convergence impacts precision health outcomes. The dialogue focuses on the main technologies supporting precision health, the latest scientific developments, technology advancements, opportunities, and risks. It is moderated by BHTY journal editor Prof. Dr. Vasiliu-Feltes. Participants emphasize the latest scientific developments, opportunities, and risks associated with precision health, enabled by converging advanced technologies. 

References

1. Cardoso ML, Martiniano H, Mota-Vieira L, Vicente AM. Genomics in health and biomedicine. Adv Exp Med Biol. 2026;1504:329-356. doi:10.1007/978-3-032-18966-0_16

2. Reardon B, Culhane AC, Van Allen EM. Convergence of machine learning and genomics for precision oncology. Nat Rev Cancer. 2026;26(3):217-229. doi:10.1038/s41568-025-00897-6

3. Sharma K, Hansen J, Susztak K, Eberlin L, Anderton CR, Alexandrov T, Iyengar R. Spatial metabolomics and multiomics integration for breakthroughs in precision medicine for kidney disease. Nat Rev Nephrol. 2026;22(2):152-164. doi:10.1038/s41581-025-01007-3

4. Baião AR, Cai Z, Poulos RC, Robinson PJ, Reddel RR, Zhong Q, Vinga S, Gonçalves E. A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches. Brief Bioinform. 2025;26(4):bbaf355. doi:10.1093/bib/bbaf355

5. Zhou J, Jiang J, Han Z, Wang Z, Gao X. Streamline automated biomedical discoveries with agentic bioinformatics. Brief Bioinform. 2025;26(5):bbaf505. doi:10.1093/bib/bbaf505

6. Gentile G, Morello G, La Cognata V, Guarnaccia M, Cavallaro S. Artificial intelligence in transcriptomics: from human-in-the-loop to agentic AI. J Pers Med. 2026;16(4):181. doi:10.3390/jpm16040181

7. Branda F, Ahmed MM, Ciccozzi M, Guzzi PH, Scarpa F. The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery. Brief Bioinform. 2026;27(3):bbag245. doi:10.1093/bib/bbag245

8. Cicin FN, Cicin I. Integrating mHealth innovations into decentralized oncology trials. J Med Syst. 2025;49(1):103. doi:10.1007/s10916-025-02233-9

9. Wang X, Xie Y, Chen X, Yang J, Li R, Gao W, Yan Z, Zhou H, Ye Z. Securing federated learning with blockchain in the medical field: systematic literature review. J Med Internet Res. 2026;28:e79052. doi:10.2196/79052

10. Chafik K, Hanine M, Ouaguid A, Alshuhri S. Blockchain technology to enhance clinical data management in healthcare: a systematic literature review. Blockchain Healthc Today. 2026;9(1). doi:10.30953/bhty.v9.471

11. Liu J, Hu X. Blockchain meets AI in healthcare: a review of convergent technologies for digital health transformation. Front Blockchain. 2026;9:1766092. doi:10.3389/fbloc.2026.1766092

12. Shahsavari Y, Baseri Y, Hafid A, Dambri O, Makrakis D. Integration of federated learning and blockchain in health care: tutorial on medical data, architectures, privacy, security, and regulatory compliance. J Med Internet Res. 2026;28:e80178. doi:10.2196/80178

13. Myrzashova R, Alsamhi SH, Shvetsov AV, Hawbani A, Wei X. Blockchain meets federated learning in healthcare: a systematic review with challenges and opportunities. IEEE Internet Things J. 2023;10(16):14418-14437. doi:10.1109/JIOT.2023.3263598

14. Nassir N, Hashmi MA, Raji KG, Jamalalail B, Maksymowsky A, Scherer SW, Alsheikh-Ali A, Uddin M. Quantum computing and the implementation of precision medicine. NPJ Genom Med. 2025;10(1):80. doi:10.1038/s41525-025-00537-w

15. Sung JY, Cheong JH. Quantum medicine: a quantum-mechanical framework for redox biology, disease and precision medicine. Clin Transl Med. 2026;16(1):e70598. doi:10.1002/ctm2.70598

16. Baldini F, Dholakia K, French P, Guntinas-Lichius O, Kohler A, Mäntele W, Marcu L, Sroka R, Umapathy S, Popp J. Shining a light on the future of biophotonics. J Biophotonics. 2025;18(7):e202500148. doi:10.1002/jbio.202500148

17. Gomase VS, Ghatule AP, Sharma R, Dhamane SP. Quantum computing in drug discovery: techniques, challenges, and emerging opportunities. Curr Drug Discov Technol. 2026;23(4):e15701638371707.

18. Khan NA, Akhtar MM, Zamani AS, Siddiqi AMU, Khan AR, Rajeyyagari S. Revolutionizing drug design: the convergence of AI and quantum computing—a systematic review. Artif Intell Precis Drug Des. 2026;2:317-326.

Published

2026-08-31

How to Cite

Vasiliu Feltes, I., Bustamante , C. D., Bischof, MD, PhD, E., & Dennis, MS, S. J. (2026). Opportunities and Risks of Technology Convergence in Precision Health. Blockchain in Healthcare Today, 9(2). https://doi.org/10.30953/bhty.v9.521

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Section

Conference Presentations