RETRACTED: Blockchain-Powered Healthcare Systems: Enhancing Scalability and Security with Hybrid Deep Learning

Area of Science:

• Health Informatics
• Computer Science
• Artificial Intelligence

Background:

• Healthcare systems face challenges in scalability, security, and data management.
• Existing centralized systems are vulnerable to data breaches and lack interoperability.
• Technological advancements are crucial for improving healthcare efficiency and patient outcomes.

Purpose of the Study:

• To propose a novel framework integrating blockchain technology and hybrid deep learning for healthcare.
• To enhance the scalability, security, and interoperability of healthcare systems.
• To improve data integrity, patient privacy, and facilitate data-driven decision-making.

Main Methods:

• Development of a permissions-based blockchain framework for secure healthcare data management.
• Integration of hybrid deep learning models for advanced data analysis and prediction.
• Implementation of access control mechanisms to preserve patient privacy.

Main Results:

• The proposed framework ensures secure, transparent, and decentralized storage and sharing of health data.
• Hybrid deep learning models enable real-time analysis for timely diagnosis and treatment recommendations.
• Demonstrated enhanced scalability, security, and interoperability compared to traditional systems.

Conclusions:

• The integration of blockchain and hybrid deep learning offers a robust solution for modern healthcare challenges.
• This approach promotes efficient data management, personalized patient care, and advancements in medical research.
• Addressing challenges like computational complexity and regulatory compliance is key for successful implementation.