Secure by design: Blockchain-AI hybrid delivers fault-tolerant medical infrastructure

As urban health systems increasingly integrate digital technologies, a new study has unveiled a hybrid architecture that leverages both artificial intelligence and blockchain to dramatically enhance secure communication and anomaly detection within Internet hospitals. The research, titled "Hybrid AI- and Blockchain-Powered Secure Internet Hospital Communication and Anomaly Detection in Smart Cities," was published in the journal Processes. It presents a modular governance model designed to secure electronic health records (EHRs), detect real-time anomalies, and scale with high-volume healthcare data in smart city environments.

The framework incorporates enhanced RSA encryption, a private Proof-of-Authority (PoA) blockchain, and the Isolation Forest anomaly detection algorithm. It aims to resolve the limitations of conventional centralized systems, such as latency, data tampering risks, and inadequate fault tolerance. Tested in simulated environments involving over 1,000 nodes, the proposed system demonstrated double the throughput and a 50% reduction in latency compared to baseline models, achieving a 98% anomaly detection accuracy.

Why are current smart healthcare systems failing in security and scalability?

Despite the promise of Internet of Things (IoT) devices in modern healthcare, providing real-time monitoring and data analytics, existing frameworks struggle with data security, system scalability, and operational resilience. Traditional centralized architectures create single points of failure and expose EHRs to tampering, replay attacks, and unauthorized access. Moreover, most healthcare systems are not designed to handle the large volumes of continuous data generated by wearable devices and remote sensors.

The study details how IoT-based Internet hospitals are uniquely vulnerable due to their reliance on low-power, distributed sensors and real-time requirements. Without robust anomaly detection, abnormal data, whether from device malfunction or genuine medical emergencies, can be overlooked. Additionally, centralized storage of medical records creates latency and cost bottlenecks, undermining the responsiveness of critical care systems.

Blockchain offers a decentralized and tamper-proof ledger system, but traditional models like Proof-of-Work (PoW) are energy-intensive and slow. The proposed framework’s shift to PoA ensures fast, energy-efficient validation by trusted healthcare nodes. By integrating Isolation Forest for continuous anomaly detection, the system proactively identifies unusual patterns in patient data, ensuring timely alerts and forensic traceability.

How does the proposed AI-blockchain system function and outperform existing models?

The framework follows a modular design with eight integrated components: IoT Data Module, Anomaly Detection Module, Blockchain Module, Data Storage and Analysis Module, Access Control Module, Central Authority Module, Feedback Loop, and Patient Module. Each module plays a specific role, forming a secure and responsive ecosystem:

• IoT Data Module: Collects and preprocesses raw data from medical sensors.

• Anomaly Detection Module: Uses the Isolation Forest algorithm to flag data anomalies.

• Blockchain Module: Encrypts and stores validated data using RSA-1024 and PoA.

• Data Storage and Analysis Module: Aggregates and analyzes data for clinical insights.

• Access Control Module: Employs role-based access to enforce strict permissions.

• Central Authority Module: Oversees validation of flagged anomalies.

• Feedback Loop: Dynamically adjusts detection thresholds and algorithms.

• Patient Module: Grants users visibility into their own health data and alerts.

In experimental trials using Google Colab Pro and a simulated network of 1,000 nodes, the framework outperformed its predecessor and several peer models. It achieved:

• Throughput: 935 transactions per second (TPS), double the baseline’s 450 TPS.

• Latency: Reduced by 50%, enabling real-time responsiveness.

• Anomaly Detection Accuracy: 98.7% via Isolation Forest algorithm.

These outcomes are attributed to efficient transaction batching, modular data flow, and low-overhead encryption. Tampering and replay attacks were successfully detected, ensuring data integrity. Comparative evaluations against leading models, such as MEDACCESSX and GM-SSO, confirmed the superiority of this hybrid framework in speed, scalability, and precision.

What are the broader implications for healthcare and smart city infrastructure?

Beyond its immediate benefits in healthcare, the framework offers a blueprint for secure, scalable digital governance across smart cities. By combining cryptographic security, real-time analytics, and decentralized trust models, the system can be adapted to other domains such as energy management, public safety, and environmental monitoring.

In Internet hospitals, it addresses core challenges of patient data protection, clinical decision support, and operational efficiency. The role-based access control ensures only authorized personnel can interact with sensitive information, while patients maintain autonomy through the Patient Module. The modular architecture also simplifies integration with legacy systems and facilitates rapid policy updates through its dynamic feedback loop.

Notably, the study acknowledges a few limitations too. PoA consensus, while efficient, introduces centralization risks due to validator dependencies. Continuous blockchain expansion may strain storage in long-term deployments. To mitigate these, the authors propose integrating off-chain storage, federated learning, and zero-knowledge proofs in future iterations. These enhancements would preserve decentralization, reduce hardware demands, and improve privacy.