AI Economics: Risk Profiles — Narrow vs General-Purpose AI Systems

Posted on by
AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665626  57stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources18%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI25%○≥80% have a Digital Object Identifier
[b]CrossRef18%○≥80% indexed in CrossRef
[i]Indexed71%○≥80% have metadata indexed
[l]Academic46%○≥80% from journals/conferences/preprints
[f]Free Access68%○≥80% are freely accessible
[r]References28 refs✓Minimum 10 references required
[w]Words [REQ]3,660✓Minimum 2,000 words for a full research article. Current: 3,660
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665626
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]11%✗≥80% of references from 2025–2026. Current: 11%
[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 (61 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Enterprise AI systems exhibit fundamentally different risk profiles depending on their architectural paradigm. This paper presents a comprehensive economic analysis comparing narrow AI systems—purpose-built for specific tasks—with general-purpose AI (GPAI) systems, particularly large language models and foundation models that have proliferated since 2022. Drawing from 14 years of enterprise sof...

Show moreHide
AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18665626 57stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources18%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI25%○≥80% have a Digital Object Identifier
[b]CrossRef18%○≥80% indexed in CrossRef
[i]Indexed71%○≥80% have metadata indexed
[l]Academic46%○≥80% from journals/conferences/preprints
[f]Free Access68%○≥80% are freely accessible
[r]References28 refs✓Minimum 10 references required
[w]Words [REQ]3,660✓Minimum 2,000 words for a full research article. Current: 3,660
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665626
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]11%✗≥80% of references from 2025–2026. Current: 11%
[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 (61 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
AI EconomicsRead More
Read more

AI Economics: Structural Differences — Traditional vs AI Software

Posted on by
AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665628  34stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted45%○≥80% from verified, high-quality sources
[a]DOI0%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed23%○≥80% have metadata indexed
[l]Academic0%○≥80% from journals/conferences/preprints
[f]Free Access36%○≥80% are freely accessible
[r]References22 refs✓Minimum 10 references required
[w]Words [REQ]3,575✓Minimum 2,000 words for a full research article. Current: 3,575
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665628
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥80% 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]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (23 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

In March 2022, a senior architect at a Fortune 500 financial services firm stood before his team with a troubling admission. His organization had spent $47 million over three years building what they called "the most sophisticated fraud detection system in the industry." The system worked—brilliantly, in fact—catching 23% more fraudulent transactions than their previous rule-based approach. But...

Show moreHide
AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18665628 34stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted45%○≥80% from verified, high-quality sources
[a]DOI0%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed23%○≥80% have metadata indexed
[l]Academic0%○≥80% from journals/conferences/preprints
[f]Free Access36%○≥80% are freely accessible
[r]References22 refs✓Minimum 10 references required
[w]Words [REQ]3,575✓Minimum 2,000 words for a full research article. Current: 3,575
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665628
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥80% 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]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (23 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
AI EconomicsRead More
Read more

Enterprise AI Risk: The 80-95% Failure Rate Problem — Introduction

Posted on by Iryna Ivchenko
AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665630  62stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources48%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI52%○≥80% have a Digital Object Identifier
[b]CrossRef48%○≥80% indexed in CrossRef
[i]Indexed56%○≥80% have metadata indexed
[l]Academic52%○≥80% from journals/conferences/preprints
[f]Free Access41%○≥80% are freely accessible
[r]References27 refs✓Minimum 10 references required
[w]Words [REQ]3,127✓Minimum 2,000 words for a full research article. Current: 3,127
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665630
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥80% 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]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (69 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Enterprise artificial intelligence initiatives fail at rates between 80% and 95%—a staggering statistic that dwarfs failure rates in traditional software development. Despite billions in investment, most AI projects never reach production, and those that do often fail to deliver promised business value. This failure epidemic is not primarily caused by limitations in machine learning algorithms ...

Show moreHide
AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18665630 62stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources48%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI52%○≥80% have a Digital Object Identifier
[b]CrossRef48%○≥80% indexed in CrossRef
[i]Indexed56%○≥80% have metadata indexed
[l]Academic52%○≥80% from journals/conferences/preprints
[f]Free Access41%○≥80% are freely accessible
[r]References27 refs✓Minimum 10 references required
[w]Words [REQ]3,127✓Minimum 2,000 words for a full research article. Current: 3,127
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665630
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥80% 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]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (69 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
AI EconomicsRead More
Read more

Data Mining Chapter 4: Taxonomic Framework Overview — Classifying the Field

Posted on by
Data Science
Data Science by Iryna Ivchenko & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665633  49stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted80%✓≥80% from verified, high-quality sources
[a]DOI0%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed80%✓≥80% have metadata indexed
[l]Academic10%○≥80% from journals/conferences/preprints
[f]Free Access90%✓≥80% are freely accessible
[r]References10 refs✓Minimum 10 references required
[w]Words [REQ]4,502✓Minimum 2,000 words for a full research article. Current: 4,502
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665633
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥80% 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]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (47 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The proliferation of data mining techniques over the past three decades has created an urgent need for systematic organization and classification of methodological approaches. This chapter establishes a comprehensive meta-taxonomic framework for understanding, categorizing, and relating the diverse landscape of data mining methods. We propose a three-dimensional classification scheme that organ...

Show moreHide
Data Science by Iryna Ivchenko & Oleh Ivchenko DOI: 10.5281/zenodo.18665633 49stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted80%✓≥80% from verified, high-quality sources
[a]DOI0%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed80%✓≥80% have metadata indexed
[l]Academic10%○≥80% from journals/conferences/preprints
[f]Free Access90%✓≥80% are freely accessible
[r]References10 refs✓Minimum 10 references required
[w]Words [REQ]4,502✓Minimum 2,000 words for a full research article. Current: 4,502
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665633
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥80% 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]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (47 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Intellectual Data …Read More
Read more

Anticipatory Intelligence: State of the Art — Current Approaches to Predictive AI

Posted on by
Academic Research
Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665635  61stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources31%○≥80% from editorially reviewed sources
[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI63%○≥80% have a Digital Object Identifier
[b]CrossRef34%○≥80% indexed in CrossRef
[i]Indexed31%○≥80% have metadata indexed
[l]Academic59%○≥80% from journals/conferences/preprints
[f]Free Access63%○≥80% are freely accessible
[r]References32 refs✓Minimum 10 references required
[w]Words [REQ]3,171✓Minimum 2,000 words for a full research article. Current: 3,171
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665635
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥80% 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]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (67 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

By Dmytro Grybeniuk, AI Architect | Anticipatory Intelligence Specialist | Stabilarity Hub | February 2026

Show moreHide
Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18665635 61stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources31%○≥80% from editorially reviewed sources
[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI63%○≥80% have a Digital Object Identifier
[b]CrossRef34%○≥80% indexed in CrossRef
[i]Indexed31%○≥80% have metadata indexed
[l]Academic59%○≥80% from journals/conferences/preprints
[f]Free Access63%○≥80% are freely accessible
[r]References32 refs✓Minimum 10 references required
[w]Words [REQ]3,171✓Minimum 2,000 words for a full research article. Current: 3,171
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665635
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥80% 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]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (67 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Anticipatory Intel…Read More
Read more

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

Posted on by
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665637  

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...

Show moreHide
Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18665637
Medical ML DiagnosisRead More
Read more

Medical ML: Training Curriculum for Medical AI — Healthcare Professional Development Framework

Posted on by
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665639  15stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted0%○≥80% from verified, high-quality sources
[a]DOI0%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic0%○≥80% from journals/conferences/preprints
[f]Free Access0%○≥80% are freely accessible
[r]References1 refs○Minimum 10 references required
[w]Words [REQ]0✗Minimum 2,000 words for a full research article. Current: 0
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665639
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥80% 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 (1 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

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...

Show moreHide
Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18665639 15stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted0%○≥80% from verified, high-quality sources
[a]DOI0%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic0%○≥80% from journals/conferences/preprints
[f]Free Access0%○≥80% are freely accessible
[r]References1 refs○Minimum 10 references required
[w]Words [REQ]0✗Minimum 2,000 words for a full research article. Current: 0
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665639
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥80% 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 (1 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
Medical ML DiagnosisRead More
Read more

Medical ML: Clinical Protocol Templates for ML-Assisted Medical Imaging Diagnosis

Posted on by
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18730553  54stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic0%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References1 refs○Minimum 10 references required
[w]Words [REQ]4,105✓Minimum 2,000 words for a full research article. Current: 4,105
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730553
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥80% 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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (56 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

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...

Show moreHide
Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18730553 54stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic0%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References1 refs○Minimum 10 references required
[w]Words [REQ]4,105✓Minimum 2,000 words for a full research article. Current: 4,105
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730553
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥80% 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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (56 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Medical ML DiagnosisRead More
Read more

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

Posted on by
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18730555  28stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted10%○≥80% from verified, high-quality sources
[a]DOI10%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed10%○≥80% have metadata indexed
[l]Academic0%○≥80% from journals/conferences/preprints
[f]Free Access10%○≥80% are freely accessible
[r]References10 refs✓Minimum 10 references required
[w]Words [REQ]4,308✓Minimum 2,000 words for a full research article. Current: 4,308
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730555
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]6%✗≥80% 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]Diagrams10✓Mermaid architecture/flow diagrams. Current: 10
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (13 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

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...

Show moreHide
Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18730555 28stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted10%○≥80% from verified, high-quality sources
[a]DOI10%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed10%○≥80% have metadata indexed
[l]Academic0%○≥80% from journals/conferences/preprints
[f]Free Access10%○≥80% are freely accessible
[r]References10 refs✓Minimum 10 references required
[w]Words [REQ]4,308✓Minimum 2,000 words for a full research article. Current: 4,308
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730555
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]6%✗≥80% 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]Diagrams10✓Mermaid architecture/flow diagrams. Current: 10
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (13 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Medical ML DiagnosisRead More
Read more

Medical ML: Comprehensive Framework for ML-Based Medical Imaging Diagnosis — Ukrainian Implementation Guide

Posted on by
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18730557  52stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources40%○≥80% from editorially reviewed sources
[t]Trusted75%○≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
[b]CrossRef35%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access20%○≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]3,414✓Minimum 2,000 words for a full research article. Current: 3,414
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730557
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]19%✗≥80% of references from 2025–2026. Current: 19%
[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 (52 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

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...

Show moreHide
Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18730557 52stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources40%○≥80% from editorially reviewed sources
[t]Trusted75%○≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
[b]CrossRef35%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access20%○≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]3,414✓Minimum 2,000 words for a full research article. Current: 3,414
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730557
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]19%✗≥80% of references from 2025–2026. Current: 19%
[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 (52 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Medical ML DiagnosisRead More
Read more