A DeSci-Driven Blockchain Framework for AI-Augmented Healthcare Research

Authors

  • Garima Singh, PhD Research Scholar, Department of Computer Science & Engineering, Integral University, Lucknow, India; Assistant Professor, Department of CSIT, Krishna Institute of Engineering and Technology, Delhi-NCR, Ghaziabad, India https://orcid.org/0000-0002-1130-6115
  • Mohd. Haroon, PhD Professor, Department of Computer Science & Engineering, Integral University, Lucknow, India
  • Nudrat Fatima, PhD Associate Professor, Department of Computer Science & Engineering, Integral University, Lucknow, India
  • Afsaruddin, PhD Assistant Professor, Department of CEA, GLA University, Mathura, India
  • Tameem Ahmad, PhD Assistant Professor, Department of Computer Engineering, Aligarh Muslim University, Aligarh, India
  • Mohammad Husain, PhD Department of Computer Science and Engineering, School of Management Sciences, Lucknow, India
  • Mahfuzul Huda, PhD Assistant Professor, Department of Computer Science, College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia

DOI:

https://doi.org/10.30953/bhty.v9.507

Keywords:

Artificial intelligence, blockchain in healthcare, decentralized governance, decentralized science (DeSCi), electronic health records (EHR), hybrid consensus mechanism, privacy-preserving data sharing, risk prediction, zeroknowledge proof (ZKP)

Abstract

 

 Background: The accelerated digital revolution in healthcare has greatly enhanced data management with electronic health records. Nonetheless, challenging issues of centralized control, privacy breaches, absence of patient ownership of their data, and inability to support decentralized scientific collaboration remain barriers to scalable healthcare research ecosystems. Recent developments in decentralized science (DeSci) create a paradigm shift, using blockchain, cryptographic primitives, and decentralized governance to facilitate transparent, trust-minimized, and collaborative biomedical research. This article provides a DeSci-friendly lightweight blockchain architecture that is used to support privacy-preserving and incentive-sensitive decentralized healthcare research infrastructure. 

Methods: The framework combines a permissioned blockchain with a lightweight hybrid consensus protocol, off-chain storage, and zero-knowledge proof-based authentication to permit secure and privacy-preserving access to data without revealing identity. Also, a tokenomics-based governance layer is proposed to support decentralized engagement, transparent policy implementation, and incentive-based research participation. The suggested system is tested in simulation with the following different network conditions and the key performance metrics such as latency, throughput, and computational cost. 

Results: Experiments prove that the suggested framework offers the following advantages: 

lower latency 

greater throughput 

enhanced computational efficiency 

 

when compared to the current blockchain-based healthcare systems such as medical records, Fast Healthcare Interoperability Resources, and HealthChain. In addition, the framework goes past traditional data management by allowing a DeSci-oriented research life cycle, such as decentralized data contribution, validation, and provenance tracking. 

Conclusions: The proposed framework will help build scalable, secure, and patient-centered decentralized healthcare research ecosystems. Furthermore, the framework bridges the gap between blockchain-based healthcare systems and DeSci-driven research ecosystems. 

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Published

2026-09-06

How to Cite

Singh, G., Haroon, M., Fatima, N., Afsaruddin, PhD, A., Ahmad, T., Husain, PhD, M., & Huda, M. (2026). A DeSci-Driven Blockchain Framework for AI-Augmented Healthcare Research. Blockchain in Healthcare Today, 9(2). https://doi.org/10.30953/bhty.v9.507

Issue

Vol. 9 No. 2 (2026): Blockchain in Healthcare Today

Section

Original Research

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Copyright (c) 2026 Garima Singh, PhD, Mohd. Haroon, PhD, Nudrat Fatima, PhD, Afsaruddin, PhD, Tameem Ahmad, PhD, Mohammad Husain, PhD, Mahfuzul Huda, PhD

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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