Medical ML: ScanLab Integration Specifications — Technical Architecture for Ukrainian Healthcare AI

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18730555  28stabilfr·wdophcgmx
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This technical specification document defines the integration architecture, interface requirements, and implementation standards for deploying artificial intelligence (AI) systems within ScanLab and similar Ukrainian diagnostic imaging facilities. Building upon the pilot program framework established in Article 30 and the comprehensive framework document in Article 31, this specification transl...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18730555 28stabilfr·wdophcgmx
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Medical ML: Comprehensive Framework for ML-Based Medical Imaging Diagnosis — Ukrainian Implementation Guide

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18730557  50stabilfr·wdophcgmx
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This paper presents the UMAID Framework (Ukrainian Medical AI Deployment) — a comprehensive, evidence-based implementation guide for machine l[REDACTED]g-based medical imaging diagnosis systems tailored specifically for the Ukrainian healthcare context. Synthesizing insights from 30 prior research articles spanning international best practices, technical architectures, clinical workflow integra...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18730557 50stabilfr·wdophcgmx
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Defining Anticipatory Intelligence: Taxonomy and Scope

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18749471  66stabilfr·wdophcgmx
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In 2019, the U.S. Intelligence Community formally adopted "Anticipatory Intelligence" as a strategic priority, defining it as the ability to "sense, anticipate, and warn of emerging conditions, trends, threats, and opportunities that may require a rapid shift in national security posture, priorities, or emphasis" [1]. Yet when the same term appears in machine l[REDACTED]g literature, healthcare...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18749471 66stabilfr·wdophcgmx
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The Black Swan Problem: Why Traditional AI Fails at Prediction

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18749477  25stabilfr·wdophcgmx
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Traditional recurrent neural network architectures—including LSTMs and GRUs—exhibit systematic failure modes when confronted with Black Swan events: rare, high-impact occurrences that fall outside the training distribution. This technical analysis quantifies the economic impact of prediction failures, examines the mathematical foundations of why these architectures fail, and introduces the conc...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18749477 25stabilfr·wdophcgmx
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Data Mining Chapter 2: Evolution of Data Mining Techniques (1960s-2000s)

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Data Science
Data Science by Iryna Ivchenko & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18749485  64stabilfr·wdophcgmx
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This chapter chronicles the remarkable metamorphosis of data mining techniques across four transformative decades, from the pioneering expert systems of the 1960s to the sophisticated ensemble methods and standardized methodologies of the early 2000s. We trace the intellectual lineage from DENDRAL's rule-based reasoning through Quinlan's revolutionary decision tree algorithms, the renaissance o...

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Data Science by Iryna Ivchenko & Oleh Ivchenko DOI: 10.5281/zenodo.18749485 64stabilfr·wdophcgmx
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Data Mining Chapter 3: The Modern Era — Big Data and Intelligent Mining

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Data Science by Iryna Ivchenko & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18749487  65stabilfr·wdophcgmx
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This chapter chronicles the revolutionary transformation of data mining during the big data era, spanning from Google's MapReduce paradigm in 2004 to the present age of intelligent, automated mining systems. We examine how the confluence of distributed computing, deep l[REDACTED]g, and cloud infrastructure fundamentally redefined both the scale and sophistication of knowledge discovery from dat...

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Data Science by Iryna Ivchenko & Oleh Ivchenko DOI: 10.5281/zenodo.18749487 65stabilfr·wdophcgmx
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[f]Free Access50%○≥80% are freely accessible
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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Data Mining Chapter 1: The Genesis of Data Mining — From Statistics to Discovery

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Data Science by Iryna Ivchenko & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18749494  63stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (71 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

This chapter traces the fascinating journey of data mining from its embryonic roots in 19th-century statistics to its crystallization as a formal discipline in the 1990s. We explore how Francis Galton's pioneering work on regression analysis and Karl Pearson's correlation coefficients laid the mathematical groundwork for pattern discovery. The narrative advances through the computational revolu...

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Data Science by Iryna Ivchenko & Oleh Ivchenko DOI: 10.5281/zenodo.18749494 63stabilfr·wdophcgmx
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[l]Academic79%○≥80% from journals/conferences/preprints
[f]Free Access36%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18749494
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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Score = Ref Trust (71 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Medical ML: Cost-Benefit Analysis of AI Implementation for Ukrainian Hospitals

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752830  54stabilfr·wdophcgmx
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[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
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[w]Words [REQ]3,590✓Minimum 2,000 words for a full research article. Current: 3,590
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752830
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]44%✗≥60% of references from 2025–2026. Current: 44%
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Score = Ref Trust (56 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The adoption of artificial intelligence in medical imaging presents Ukrainian healthcare institutions with a complex economic decision. This article provides a comprehensive cost-benefit analysis framework specifically designed for the Ukrainian healthcare context, accounting for the country's unique economic conditions, wartime constraints, and institutional structures. We examine the total co...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752830 54stabilfr·wdophcgmx
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[s]Reviewed Sources33%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
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[w]Words [REQ]3,590✓Minimum 2,000 words for a full research article. Current: 3,590
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]44%✗≥60% of references from 2025–2026. Current: 44%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (56 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Medical ML: Legal Framework for AI in Ukrainian Healthcare — Regulations, Liability, and EU Harmonization

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752832  51stabilfr·wdophcgmx
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[s]Reviewed Sources26%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI37%○≥80% have a Digital Object Identifier
[b]CrossRef26%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic37%○≥80% from journals/conferences/preprints
[f]Free Access79%○≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]4,567✓Minimum 2,000 words for a full research article. Current: 4,567
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752832
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]5%✗≥60% of references from 2025–2026. Current: 5%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (50 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Odesa National Polytechnic University (ONPU) Stabilarity Hub Research Initiative Medical ML Diagnostic Systems Research Program

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752832 51stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources26%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI37%○≥80% have a Digital Object Identifier
[b]CrossRef26%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic37%○≥80% from journals/conferences/preprints
[f]Free Access79%○≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]4,567✓Minimum 2,000 words for a full research article. Current: 4,567
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752832
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]5%✗≥60% of references from 2025–2026. Current: 5%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (50 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Medical ML: Language Localization for Ukrainian Medical AI User Interfaces

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18704562  42stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources14%○≥80% from editorially reviewed sources
[t]Trusted52%○≥80% from verified, high-quality sources
[a]DOI29%○≥80% have a Digital Object Identifier
[b]CrossRef14%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic29%○≥80% from journals/conferences/preprints
[f]Free Access33%○≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]4,976✓Minimum 2,000 words for a full research article. Current: 4,976
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18704562
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]9%✗≥60% of references from 2025–2026. Current: 9%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (35 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The successful deployment of machine l[REDACTED]g-based diagnostic systems in Ukrainian healthcare facilities requires comprehensive language localization that extends far beyond simple text translation. This article presents a systematic framework for adapting medical AI user interfaces to the Ukrainian linguistic and cultural context, addressing the unique challenges posed by Cyrillic script ...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18704562 42stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources14%○≥80% from editorially reviewed sources
[t]Trusted52%○≥80% from verified, high-quality sources
[a]DOI29%○≥80% have a Digital Object Identifier
[b]CrossRef14%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic29%○≥80% from journals/conferences/preprints
[f]Free Access33%○≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]4,976✓Minimum 2,000 words for a full research article. Current: 4,976
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18704562
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]9%✗≥60% of references from 2025–2026. Current: 9%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (35 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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