HEAL-AI: Enabling proactive and personalized smart healthcare through hierarchical edge autonomous learning

Area of Science:

• Artificial Intelligence in Healthcare
• Federated Learning and Edge Computing
• Neuro-Symbolic AI

Background:

• Distributed healthcare environments require proactive, personalized, and privacy-preserving decision support.
• Existing systems face challenges in real-time inference, interpretability, and regulatory compliance.

Purpose of the Study:

• To develop HEAL-AI, a smart healthcare framework integrating federated learning, neuro-symbolic AI, and edge intelligence.
• To enable real-time clinical decision support across distributed settings while ensuring privacy and interpretability.

Main Methods:

• HEAL-AI utilizes federated learning for collaborative training without raw data sharing.
• It incorporates hierarchical contextual attention, symbolic reasoning, and edge intelligence (model compression, adaptive offloading).
• Privacy is maintained via secure aggregation and differential privacy.

Main Results:

• HEAL-AI outperformed baselines, improving diagnostic accuracy by up to 17.3% and early sepsis detection by 5.9 hours.
• Inference latency and energy consumption were reduced by 32.6% on edge devices.
• Explainability improved by 43.5%, with 94.3% symbolic rule coverage and 94.4% reduction in privacy leakage.

Conclusions:

• Federated neuro-symbolic learning with edge intelligence offers accurate, explainable, and private healthcare AI.
• HEAL-AI is suitable for critical care, remote monitoring, and personalized clinical decision support.
• The framework addresses key challenges in real-world AI deployment in healthcare.