Figure 1.
The major key challenges, blockchain-enabled opportunities, and emerging privacy-preserving and scalability-enhancing technologies in smart healthcare systems
Figure 2.
The PRISMA-inspired literature selection process was followed in this review
Figure 3.
Publication Trends in Healthcare Blockchain (2019–2025)
Figure 4.
Publication Trends in Healthcare Blockchain (2019–2025) Stacked Area view
Figure 5.
Publication Trends in Healthcare Blockchain (2019-2025) Log Scale
Table 1.
Privacy Models in Blockchain-Based Healthcare Systems (2019–2025)
| Technique | Principles | Strengths | Limitations | Representative Studies |
|---|---|---|---|---|
| Access Control (Smart Contracts, ABE) | Access policies enforced via smart contracts;ABE links access to roles/attributes | Fine-grained sharing; strong patient autonomy | Emergency access complexity; crosschain interoperability issues | [19–21] |
| Homomorphic Encryption (HE) | Computation performed directly on encrypted EHR data | Enables secure analytics and ML without decryption | High computational overhead; unsuitable for real-time monitoring | [22–24] |
| Secure Multi-Party Computation (SMPC) | Collaborative computation without sharing raw datasets | Preserves privacy in multi-institution studies | Communication latency; scalability limitations | [25–27] |
| Hybrid (Differential Privacy + Blockchain) | Noise added to data prior to storage or analysis | Lightweight; protects IoMT data streams | Reduced data utility; tuning complexity | [28–30] |
| Hybrid (Encryption + Off-chain Storage) | Blockchain stores hashes/metadata; data stored off-chain | Scalable; efficient storage; auditable | External trust dependency; linkability risks | [31, 32] |
Table 2.
Comparison of ZKPs and Ring Signatures in Healthcare Blockchains(2019–2025)
| Technique | Principles | Strengths | Limitations | Representative Studies |
|---|---|---|---|---|
| Zero-Knowledge Proofs (ZKPs) | Prover convinces verifier of a statement (e.g., consent, correct computation) without revealing underlying data. | Strong selective disclosure (prove consent/qualification without revealing identity or content); enables verifiable off chain computation (e.g., FL update checks); ZK rollups can improve throughput while preserving privacy. | Proof generation can be computationally expensive (hard for IoMT); verifier/transition costs add latency; engineering complexity (tooling, trusted setup for some ZK systems); sparse real EHR integrations. | [33–36] |
| Ring Signatures (including linkable variants) | A signer produces a signature that proves membership in a set but conceals which member signed; linkable versions allow detection of double use without deanonymizing. | Provides unlinkable transaction anonymity (prescriptions, claims); simpler and more compact verification than many HE/SMPC schemes; linkable variants add fraud detection while preserving anonymity. | Larger signature sizes than classical ECDSA; key management in dynamic consortia is non trivial; linkability vs. accountability trade offs need governance rules; not broadly standardised on permissioned platforms. | [37–38] |
Table 3.
Comparative Analysis of Blockchain Techniques in Healthcare (2019–2025)
| Technique | Privacy Strength | Scalability | Computation Cost | Adoption Readiness | Representative Studies |
|---|---|---|---|---|---|
| Access Control + ABE | Moderate-High (fine grained control) | Moderate (policies scale poorly across chains) | Low-Moderate | High (easy integration with EHRs) | [19–21] |
| Homomorphic Encryption | Very High (data never decrypted) | Low (latency sensitive) | Very High (orders of magnitude higher than plaintext ops) | Low (few pilots) | [22–24] |
| SMPC | High (multi hospital privacy preserved) | Low-Moderate (communication bottlenecks) | High | Medium (tested in multi hospital studies) | [25–27] |
| Differential Privacy + Blockchain | Moderate-High (depends on noise budget) | High (lightweight) | Low | Medium-High (suitable for IoMT streams) | [28–30] |
| Hybrid On-chain/Off-chain Storage | Moderate (privacy tied to off chain trust) | High (IPFS/cloud scalability) | Low | High (already tested in Hyperledger pilots) | [31–32] |
| ZKPs (Consent/Access Proofs) | Very High (no disclosure of identity/data) | Moderate | Moderate-High (proof generation overhead) | Medium (prototype level) | [33–36] |
| Ring Signatures | High | Moderate (signature size grows) | Moderate | Low-Medium (few healthcare pilots yet) | [37–38] |
Table 4.
Comparison of ZKPs and Ring Signatures in Healthcare Blockchains (2019–2025)
| Technique | Principles | Strengths | Limitations | Representative Studies |
|---|---|---|---|---|
| Layer 2 Solutions (Channels, Sidechains) | Move frequent interactions off chain, anchor summaries on chain. | High throughput; reduced latency; cost efficient for IoMT. | Auditability concerns; relies on trusted off chain operators; regulatory compliance gap. | [39–41] |
| Sharding | Partition blockchain into shards (e.g., hospital/regional clusters) for parallel processing. | Linear scalability; natural fit for healthcare networks. | Cross shard query consistency difficult; vulnerable if a shard is compromised; synchronization overhead. | [42–44] |
| Consensus Optimisation (PBFT, PoA, PoET) | Modify or replace consensus to reduce communication or energy cost. | Lower latency; energy efficient; tailored for permissioned healthcare settings. | PBFT fails at large scale; PoA/PoET reduce decentralization; limited real-world validation. | [45–47] |
| Hybrid Models | Dynamically switch between consensus algorithms (e.g., PBFT ↔ PoA/PoET). | Adaptive to workload; balances security and throughput; suitable for national EHR. | Implementation complexity; security risks during switching; not tested at production scale. | [48, 49] |
Figure 6.
Layered taxonomy of privacy-preserving blockchain architectures for healthcare
Figure 7.
Positioning of Approaches: Privacy vs Interoperability
Table 5.
Comparative Analysis of Federated Learning and Cross-Chain Interoperability in Healthcare
| Approach | Core Concept | Healthcare Applications | Privacy Preservation | Interoperability Support | Key Limitations | References |
|---|---|---|---|---|---|---|
| Federated Learning (FL) | Distributed model training without data sharing | Disease prediction, medical imaging | High | Low | Trust and auditability gaps | [50–52] |
| FL + Blockchain | Blockchain based coordination and audit of FL | Medical imaging, healthcare IoT | High | Low-Moderate | Ledger overhead, latency | [53–55] |
| Blockchain-Assisted FL | Secure logging of training rounds and updates | Device monitoring, analytics | Very High | Low | Scalability constraints | [56–58] |
| Cross-Chain Healthcare Systems | Interoperable EHR exchange across blockchains | Cross-hospital EHR sharing | Moderate | High | Bridge security risks | [59, 60] |
| Integrated FL + Interoperability | Local FL with cross-chain coordination | Multiinstitution analytics | High | Moderate-High | Cross-chain latency, limited evaluation | [61–63] |
Table 6.
Comparative Overview of Blockchain Frameworks in Healthcare (2019–2025)
| Framework / Use Case | Platform / Architecture | Focus Area | Strengths | Limitations | Adoption Readiness | Reference |
|---|---|---|---|---|---|---|
| Hyperledger Fabric EHR Systems | Permissioned blockchain (Fabric/Sawtooth) with off-chain storage | Secure EHR exchange across hospitals | Fine-grained access, audit trails, HL7/FHIR compliance | Scalability limits, complex deployment | High (pilot deployments exist) | [64–65] |
| Ethereum Telemedicine Platforms | Public/consortium Ethereum smart contracts | Teleconsultations, e-prescriptions, billing | Transparency, patient-doctor trust, micropayments | High gas cost, privacy issues | Medium (prototypes tested) | [66, 67] |
| ZKP-Enhanced Healthcare Chains | Permissioned or hybrid blockchain with ZKPs | Consent verification, anonymized access | Strong privacy, regulatory compliance | Proof size overhead, limited pilots | Low (research stage) | [68–70] |
| FL + Blockchain Prototypes | Hybrid blockchain + federated learning | Collaborative AI without raw data sharing | Privacy-preserving AI, on-chain model logging | Non-IID data, poisoning attacks, latency issues | Medium (academic demos) | [71–73] |
| IoMT Data Integrity Chains | Lightweight blockchain integrated with IoT gateways | Real-time sensor/device data | Real-time auditing, integrity assurance | Edge resource constraints, latency for critical care | Medium | [74–77] |
| Cross-Chain Interoperability | Cosmos/Polkadot-inspired relay frameworks | EHR sharing across heterogeneous blockchains | Interoperability, patient record continuity | Security of bridges, high complexity | Low-Medium (early-stage research) | [78–80] |
Table 7.
Limitations in Blockchain based Healthcare Literature (2019–2025)
| Technique / Framework | Observed Limitations | Analytical Implications for Healthcare Deployment |
|---|---|---|
| Scalability (PBFT, PoA, PoET, Hybrid Consensus) | Evaluations restricted to small networks (<50 nodes); PBFT incurs quadratic communication overhead; PoA/PoET reduce decentralization | Limited evidence of reliability and latency performance in large hospital networks; unsuitable for nationwide or IoMT-intensive healthcare scenarios without adaptive or hierarchical consensus designs |
| Privacy Models (HE, SMPC, DP, ABE) | HE and SMPC impose high computation and communication costs; DP reduces data utility; ABE complicates emergency access | Strong privacy guarantees come at the expense of real-time responsiveness; current designs struggle to balance confidentiality with clinical usability in latency sensitive environments |
| Federated Learning + Blockchain | Validation mostly on synthetic or small-scale datasets; non-IID data and model poisoning insufficiently addressed | Trust and auditability improve, but lack of real EHR validation limits clinical credibility; robustness against adversarial participants remains an open issue |
| Cross-Chain Interoperability | Predominantly conceptual frameworks; absence of healthcare-specific interoperability standards; high coordination latency | Enables institutional autonomy but lacks semantic consistency and performance guarantees required for cross-hospital clinical workflows |
| ZKPs and Ring Signatures | High proof generation and verification cost; ring signatures scale poorly with participant size | Suitable for selective verification and auditing, but impractical for continuous clinical transactions or large healthcare consortia without lightweight adaptations |
| Existing Platforms (Hyperledger, Ethereum, Bespoke Frameworks) | Partial centralization in permissioned systems; high transaction cost in public blockchains; bespoke designs lack standardization | Platform-level constraints limit portability and long term sustainability; healthcare-specific customization remains fragmented |
| Regulatory and Ethical Compliance | Limited embedding of GDPR, HIPAA, or DISHA requirements; emergency “break-glass” access poorly formalized | Legal ambiguity and unclear accountability reduce institutional trust and hinder clinical adoption despite technical feasibility |
Figure 8.
Research Gaps Map: Under explored intersections in blockchain enabled healthcare systems (2019–2025). Research landscape mapping blockchain in healthcare. The x axis represents interoperability and deployment readiness, while the y axis indicates privacy and security rigor
Table 8.
Research Questions and Findings Summary
| Research Question | Key Findings |
|---|---|
| [RQ1]. | Privacy Models: Multiple models exist, including access control with Attribute-Based Encryption (ABE), Homomorphic Encryption (HE), Secure Multi-Party Computation (SMPC), differential privacy, and hybrid approaches. Strong cryptography (HE, SMPC) ensures high privacy but incurs high computation and limited scalability. Lightweight models (DP, hybrid off-chain storage) are usable but weaker in confidentiality. Hybrid models optimized for healthcare remain underdeveloped. |
| [RQ2]. | Scalability Strategies: Layer 2 solutions (channels, sidechains) and sharding improve throughput.Consensus optimizations (PBFT, PoA, PoET) reduce latency but trade decentralization. Hybrid consensus approaches (e.g., PBFT↔PoET switching) show promise but lack validation at production scale. Overall, real-world deployment at national or hospital network scales remains limited. |
| [RQ3]. | Emerging Technologies & Advanced Cryptography: Federated learning aided by blockchain allows collaborative AI without the exchange of raw data but suffers such problems as non-IID data and model poisoning. Cross-chain interoperability models are primarily prototypes which have latency and security issues. The probability of zero-knowledge proofs (ZKPs) and ring signatures offers selective disclosure of transactions and unlinkable transactions, but has seen very little real-world use. The combination of these methods with FL and adaptive consensus is mostly done on a case-by-case basis. |
| [RQ4]. | Research Gaps & Future Directions: Key gaps include: (i) limited deployment of advanced cryptography in real hospital networks; (ii) unresolved privacy-scalability trade-offs; (iii) weak interoperability with standards such as HL7 FHIR and DICOM; and (iv) lack of regulation-aware and sustainability-focused design. Future directions include lightweight ZKPs, poisoning-resistant federated learning, adaptive consensus mechanisms, post-quantum cryptography, and compliant, energy-efficient blockchain infrastructures. |