In my master's degree, I am working on a line of research, which will include the development of a HUB of healthcare solutions, based on the use of AI and Blockchain.
I am developing the skills to implement these technologies with a focus on health, and what would be the best path forward, so that scalability is fluid throughout the entire development process.
That sounds like a really promising research direction. Combining AI and blockchain for healthcare has a lot of potential, especially in improving data security, diagnostics, and system interoperability. To make sure your solution can scale smoothly, I’d suggest focusing on building it in a modular way where different parts of your system (like the AI models or blockchain components) can function independently and be upgraded or expanded over time.
For the blockchain side, using a permissioned ledger could be a good choice. For AI, think about models that can be accessed via APIs or even deployed at the edge, depending on the use case.
So overall, I’d say: keep it modular, plan for compliance, and think about automation early. That’ll make your solution more flexible and scalable in the long run.
André Vinhosa
To boost efficiency in AI and blockchain-powered healthcare prediction, the approach focuses on a decentralized, modular, and context-aware framework:
Your Goal Should Be:
- Privacy-preserving, scalable, interoperable, and adaptive systems that continuously learn and integrate with existing healthcare infrastructure.
To boost efficiency in healthcare prediction, combine AI and Blockchain strategically. Use AI to analyze medical data (EHRs, imaging, genomics) for accurate predictions, and implement federated learning to train models across institutions without sharing sensitive data. Integrate explainable AI to improve trust in clinical decisions.
Leverage Blockchain for data security, transparency, and control. Store hashes of patient records on-chain to ensure integrity, and use smart contracts to manage data access automatically and securely. This empowers patients with ownership over their health data.
For maximum efficiency, connect both technologies: validate data via Blockchain before feeding it to AI, and log AI model inputs/outputs on-chain for auditability. Use edge computing and IoT devices to enable real-time predictions while storing data securely.
Technologies like Ethereum, Hyperledger Fabric, TensorFlow, and IPFS support this integration. This combination ensures secure, real-time, and trustworthy healthcare predictions.
Well, there are
four best ways to increase efficiency in healthcare prediction using AI and Blockchain are:
1. Secure and Distributed Data Access
2. Privacy-Preserving Collaborative Learning (federated learning)
3. Transparent and Trustworthy AI Models
Blockchain provides an immutable record of AI model development, including training data and version history. This transparency builds trust, ensures integrity, and supports regulatory audits.
4. Automated, Real-Time Decision Support
Smart contracts on Blockchain can trigger timely clinical actions based on AI predictions, reducing delays and enhancing patient care with minimal manual intervention.
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