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

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.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-02-17
DOI
https://doi.org/10.5281/zenodo.18669067
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
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article

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

ANDRII ARTSYBASHEV
Zenodo (CERN European Organization for Nuclear Research)
Radiomics and Machine Learning in Medical Imaging
article

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

ANDRII ARTSYBASHEV
article en

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.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 53%
Radiomics and Machine Learning in Medical Imaging
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