Medical ML: Open Questions for Future Research — A Medical AI Research Agenda for Ukrainian Healthcare

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665637  49stabilfr·wuedophcgmx
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After twelve weeks examining machine learning applications in medical imaging diagnosis, significant knowledge gaps remain that demand systematic investigation. This concluding article synthesizes open research questions emerging from our comprehensive review, organized across seven priority domains: generalization and distribution shift, algorithmic fairness and bias mitigation, human-AI colla...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18665637 49stabilfr·wuedophcgmx
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Medical ML: Training Curriculum for Medical AI — Healthcare Professional Development Framework

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665639  47stabilfr·wuedophcgmx
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The rapid proliferation of AI-enabled medical devices—exceeding 1,200 FDA authorizations as of 2026 with 80% targeting radiology—has outpaced the educational infrastructure needed to prepare healthcare professionals for effective utilization. A 2026 survey revealed that approximately 24% of radiology residents report having no AI/ML educational offerings in their residency programs, despite the...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18665639 47stabilfr·wuedophcgmx
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Medical ML: Clinical Protocol Templates for ML-Assisted Medical Imaging Diagnosis

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18730553  52stabilfr·wuedophcgmx
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The deployment of machine learning algorithms in clinical radiology represents one of the most significant technological transformations in modern healthcare. With over 1,200 FDA-authorized AI medical devices and hundreds of CE-marked solutions available globally, healthcare facilities face a critical challenge: translating technological capability into reliable, safe, and efficient clinical pr...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18730553 52stabilfr·wuedophcgmx
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Medical ML: ScanLab Integration Specifications — Technical Architecture for Ukrainian Healthcare AI

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18730555  42stabilfr·wuedophcgmx
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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 42stabilfr·wuedophcgmx
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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  62stabilfr·wuedophcgmx
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This paper presents the UMAID Framework (Ukrainian Medical AI Deployment) — a comprehensive, evidence-based implementation guide for machine learning-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 integration...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18730557 62stabilfr·wuedophcgmx
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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  78stabilfr·wuedophcgmx
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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 learning literature, healthcare inf...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18749471 78stabilfr·wuedophcgmx
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The Black Swan Problem: Why Traditional AI Fails at Prediction

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Academic Research
Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18749477  47stabilfr·wuedophcgmx
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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 47stabilfr·wuedophcgmx
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[f]Free Access64%○≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
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Score = Ref Trust (24 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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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  76stabilfr·wuedophcgmx
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[a]DOI86%✓≥80% have a Digital Object Identifier
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[i]Indexed7%○≥80% have metadata indexed
[l]Academic86%✓≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]5,740✓Minimum 1,000 words of article body. Current: 5,740
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18749485
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness6%○≥60% of references from 2025–2026. Current: 6%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (72 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

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 76stabilfr·wuedophcgmx
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[s]Reviewed Sources43%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI86%✓≥80% have a Digital Object Identifier
[b]CrossRef71%○≥80% indexed in CrossRef
[i]Indexed7%○≥80% have metadata indexed
[l]Academic86%✓≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]5,740✓Minimum 1,000 words of article body. Current: 5,740
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]24✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 24
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18749485
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness6%○≥60% of references from 2025–2026. Current: 6%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
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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  78stabilfr·wuedophcgmx
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[s]Reviewed Sources43%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI64%○≥80% have a Digital Object Identifier
[b]CrossRef57%○≥80% indexed in CrossRef
[i]Indexed57%○≥80% have metadata indexed
[l]Academic86%✓≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]5,787✓Minimum 1,000 words of article body. Current: 5,787
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18749487
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[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
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[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 (76 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

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 learning, and cloud infrastructure fundamentally redefined both the scale and sophistication of knowledge discovery from data. T...

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Data Science by Iryna Ivchenko & Oleh Ivchenko DOI: 10.5281/zenodo.18749487 78stabilfr·wuedophcgmx
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[s]Reviewed Sources43%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI64%○≥80% have a Digital Object Identifier
[b]CrossRef57%○≥80% indexed in CrossRef
[i]Indexed57%○≥80% have metadata indexed
[l]Academic86%✓≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]5,787✓Minimum 1,000 words of article body. Current: 5,787
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]23✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 23
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18749487
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness0%○≥60% of references from 2025–2026. Current: 0%
[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 (76 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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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  75stabilfr·wuedophcgmx
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[s]Reviewed Sources43%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI86%✓≥80% have a Digital Object Identifier
[b]CrossRef64%○≥80% indexed in CrossRef
[i]Indexed7%○≥80% have metadata indexed
[l]Academic79%○≥80% from journals/conferences/preprints
[f]Free Access36%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]4,795✓Minimum 1,000 words of article body. Current: 4,795
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]22✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 22
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18749494
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[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness7%○≥60% of references from 2025–2026. Current: 7%
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[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 (71 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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 75stabilfr·wuedophcgmx
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[s]Reviewed Sources43%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI86%✓≥80% have a Digital Object Identifier
[b]CrossRef64%○≥80% indexed in CrossRef
[i]Indexed7%○≥80% have metadata indexed
[l]Academic79%○≥80% from journals/conferences/preprints
[f]Free Access36%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]4,795✓Minimum 1,000 words of article body. Current: 4,795
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]22✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 22
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18749494
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness7%○≥60% of references from 2025–2026. Current: 7%
[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 (71 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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