A fuzzy logic and blockchain-enhanced framework for secure, explainable eHealth in Society 5.0

Area of Science:

• Intelligent Healthcare Systems
• Society 5.0
• Explainable Artificial Intelligence (XAI)

Background:

• Current intelligent healthcare systems face challenges like cloud system delays, single points of failure, and data privacy concerns.
• Traditional AI models in healthcare often lack transparency and explainability, hindering physician trust and patient acceptance.
• Existing systems struggle with real-time data processing from the Internet of Medical Things (IoMT).

Purpose of the Study:

• To propose a novel framework for expanding eHealth within the Society 5.0 paradigm.
• To address limitations of centralized cloud systems and black-box AI models in healthcare.
• To enhance the security, privacy, responsiveness, and interpretability of intelligent healthcare systems.

Main Methods:

• A multi-tiered architecture combining cloud services, edge computing, and IoMT.
• The Health Prediction using Cloud Edge 2.0 (HPCE 2.0) algorithm using fuzzy logic to integrate Electronic Health Records (EHRs) and IoMT data.
• A blockchain-enhanced architecture with Proof of Authentication 2.0 (PoAh 2.0) for data integrity and Explainable AI (LIME/SHAP) for transparency.

Main Results:

• The HPCE 2.0 algorithm accurately predicts individual health severity by handling data uncertainty.
• The PoAh 2.0 consensus mechanism ensures data integrity and non-repudiation on a decentralized ledger.
• Edge-cloud integration significantly reduces delay for critical real-time alerts, validated by security tests.

Conclusions:

• The proposed framework establishes a new standard for interpretable, secure, and responsive AI-driven healthcare.
• The system effectively addresses privacy concerns while maintaining high predictive performance, as demonstrated in a cardiac arrest prediction case study.
• Integration of XAI transforms AI from a black box into an open collaborator, fostering trust and acceptance.