What question did this study set out to answer?

The aim is to present a validation framework that checks internal consistency and estimates uncertainty in data processing systems.

February 19, 2026Open Access

Adaptive Analytical Model (AAM-V1): A Foundational Validation Framework with MRI-Oriented Applied Instantiation

Key Points

The aim is to present a validation framework that checks internal consistency and estimates uncertainty in data processing systems.
Introduced the core theoretical ontology for the Adaptive Analytical Model (AAM-V1).
Developed an applied instantiation for MRI image analysis.
Focused on internal validation and audit mechanisms within medical imaging pipelines.
Demonstrated operationalization of AAM framework within MRI processing.
Provided a systematic approach to compare and validate imaging data.
Highlighted artificial intelligence as a supportive tool rather than a theoretical contributor.

Abstract

This work introduces the Adaptive Analytical Model (AAM-V1) — a foundational, domain-agnostic validation framework designed for internal consistency checking, uncertainty esti-mation, and comparative analysis in complex data-processing systems. Version v0.1 formal-izes the core theoretical ontology of AAM as a universal analytical construct independentof any applied domain, defining the principles of reference-based validation and the FormInvariant (F). Version v0.2 presents an applied instantiation in the context of MRI imageanalysis, demonstrating how the foundational theory can be operationalized as an internalvalidation and audit layer within medical imaging pipelines. The framework is designed as anauxiliary reference mechanism; artificial intelligence systems are treated strictly as assistivecomputational tools and are not considered authors or theoretical contributors.

Adaptive Analytical Model (AAM-V1): A Foundational Validation Framework with MRI-Oriented Applied Instantiation

Key Points

Abstract

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