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A Prescriptive Validation Framework for a Scalable Multi-Layer AI Adoption Model in 6P Medicine.

Aly Khalifa¹, Rada Hussein²

¹Department of Artificial Intelligence and Informatics, Mayo Clinic, 200 1St Street SW, Rochester, Minnesota, 55905, USA.

Studies in Health Technology and Informatics

|July 3, 2026

Summary

This summary is machine-generated.

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Data Validation

Data Validation

Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...

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This study presents a validation framework for AI adoption in 6P Medicine, ensuring trustworthy, scalable, and ethical implementation. The framework guides real-world AI validation across technical, sociotechnical, and ethical aspects for improved healthcare.

Area of Science:

Healthcare Informatics
Artificial Intelligence in Medicine
Health Systems Engineering

Background:

Artificial intelligence (AI) integration is key to advancing 6P Medicine (Predictive, Preventive, Personalized, Participatory, Precision-oriented, Public-centered care).
Existing AI adoption models lack prescriptive guidance for real-world validation across technical, sociotechnical, and ethical domains.
A scalable, multi-layer AI adoption model is needed to facilitate comprehensive validation for 6P Medicine.

Purpose of the Study:

To introduce a comprehensive validation framework for the Scalable Multi-Layer AI Adoption Model for 6P Medicine.
To align architectural prerequisites, regulatory governance, and lifecycle monitoring with the model's six layers.
To guide the validation of AI in healthcare across multiple critical dimensions.

Keywords:

6P Medicine Artificial Intelligence Governance MLOps Sociotechnical Systems Validation Framework

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Main Methods:

The framework integrates architectural requirements, regulatory governance, and continuous monitoring.
Validation is structured around data integrity, model robustness, clinical efficacy, human-AI collaboration, scalability, and ethical governance.
The approach incorporates FDA Good Machine Learning Practice (GMLP) and WHO regulatory considerations.

Main Results:

The proposed framework provides a structured approach for validating AI adoption in 6P Medicine.
It addresses technical, sociotechnical, and ethical dimensions essential for real-world AI implementation.
The framework supports continuous, real-world validation processes.

Conclusions:

The validation framework enables trustworthy, scalable, and ethically governed AI integration in 6P Medicine.
It facilitates the practical realization of AI-driven healthcare advancements.
This work positions AI as a critical enabler for the future of personalized and public-centered medicine.