A Scalable Multi-Layer AI Adoption Model to Support the Comprehensive Goals of 6P Medicine

Area of Science:

• Artificial Intelligence in Healthcare
• Digital Health Transformation
• Computational Medicine

Background:

• The integration of Artificial Intelligence (AI) into 6P medicine (Predictive, Preventive, Personalized, Participatory, Precision-oriented, and Public-centered) presents complex challenges.
• Existing frameworks often lack the scalability and flexibility required for diverse healthcare applications.
• Addressing computational infrastructure, data integration, security, and interoperability is crucial for successful AI adoption.

Purpose of the Study:

• To propose a scalable multi-layer AI adoption model tailored for 6P medicine.
• To provide a flexible conceptual framework for implementing AI across various healthcare domains.
• To ensure scalability, security, governance, and efficiency in AI-driven healthcare solutions.

Main Methods:

• Development of a multi-layer architectural model for AI adoption.
• Consideration of key factors: computational infrastructure, data processing, healthcare requirements, security, privacy, performance, and interoperability.
• Conceptual design focusing on flexibility and adaptability.

Main Results:

• A flexible, multi-layer conceptual model for AI adoption in 6P medicine.
• The model addresses critical implementation factors including infrastructure, data, security, and interoperability.
• Demonstrated potential for scalability, security, governance, and efficiency.

Conclusions:

• The proposed multi-layer AI architecture offers a robust and adaptable solution for 6P medicine.
• Successful AI implementation in healthcare necessitates a holistic approach considering technical and operational aspects.
• The model facilitates the integration of AI across diverse healthcare settings while ensuring key performance and security metrics.