Category: Medical ML Diagnosis

ML for Medical Imaging Diagnosis

Tattoo-Based Emergency Patient Identification: From Internal Research to Public Deployment

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18929669  70stabilfr·wuedophcgmx
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[s]Reviewed Sources25%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI58%○≥80% have a Digital Object Identifier
[b]CrossRef25%○≥80% indexed in CrossRef
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[f]Free Access75%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]1,986✓Minimum 1,000 words of article body. Current: 1,986
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18929669
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness10%○≥60% of references from 2025–2026. Current: 10%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (62 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

We describe the public release of a tattoo-based emergency patient identification framework whose conceptual roots trace to OTG-bot — a UNDP-grant-winning civic technology project developed in 2021 for Ukrainian territorial communities. That project received a $10,000 USD grant from the United Nations Development Programme at the Hack Locals 2.0 hackathon and included an automated missing-perso...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18929669 70stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources25%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI58%○≥80% have a Digital Object Identifier
[b]CrossRef25%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic58%○≥80% from journals/conferences/preprints
[f]Free Access75%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]1,986✓Minimum 1,000 words of article body. Current: 1,986
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]8✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 8
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18929669
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness10%○≥60% of references from 2025–2026. Current: 10%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (62 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Medical ML: Open Questions for Future Research — A Medical AI Research Agenda for Ukrainian Healthcare

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665637  49stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI25%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic25%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References4 refs○Minimum 10 references required
[w]Words [REQ]3,350✓Minimum 1,000 words of article body. Current: 3,350
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]25✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 25
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665637
[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]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (27 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

After twelve weeks examining machine l[REDACTED]g 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 c...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18665637 49stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI25%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic25%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References4 refs○Minimum 10 references required
[w]Words [REQ]3,350✓Minimum 1,000 words of article body. Current: 3,350
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]25✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 25
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665637
[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]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (27 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Medical ML: Training Curriculum for Medical AI — Healthcare Professional Development Framework

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665639  47stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted40%○≥80% from verified, high-quality sources
[a]DOI20%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic20%○≥80% from journals/conferences/preprints
[f]Free Access80%✓≥80% are freely accessible
[r]References5 refs○Minimum 10 references required
[w]Words [REQ]3,256✓Minimum 1,000 words of article body. Current: 3,256
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]25✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 25
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665639
[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 (23 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18665639 47stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted40%○≥80% from verified, high-quality sources
[a]DOI20%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic20%○≥80% from journals/conferences/preprints
[f]Free Access80%✓≥80% are freely accessible
[r]References5 refs○Minimum 10 references required
[w]Words [REQ]3,256✓Minimum 1,000 words of article body. Current: 3,256
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]25✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 25
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665639
[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 (23 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Medical ML: Clinical Protocol Templates for ML-Assisted Medical Imaging Diagnosis

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18730553  52stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]4,028✓Minimum 1,000 words of article body. Current: 4,028
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]25✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 25
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730553
[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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

The deployment of machine l[REDACTED]g 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 clinica...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18730553 52stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]4,028✓Minimum 1,000 words of article body. Current: 4,028
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]25✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 25
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730553
[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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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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
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted15%○≥80% from verified, high-quality sources
[a]DOI10%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic10%○≥80% from journals/conferences/preprints
[f]Free Access25%○≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]3,973✓Minimum 1,000 words of article body. Current: 3,973
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]25✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 25
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730555
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness5%○≥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]Diagrams10✓Mermaid architecture/flow diagrams. Current: 10
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (15 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18730555 42stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted15%○≥80% from verified, high-quality sources
[a]DOI10%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic10%○≥80% from journals/conferences/preprints
[f]Free Access25%○≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]3,973✓Minimum 1,000 words of article body. Current: 3,973
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]25✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 25
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730555
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness5%○≥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]Diagrams10✓Mermaid architecture/flow diagrams. Current: 10
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (15 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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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
BadgeMetricValueStatusDescription
[s]Reviewed Sources36%○≥80% from editorially reviewed sources
[t]Trusted68%○≥80% from verified, high-quality sources
[a]DOI45%○≥80% have a Digital Object Identifier
[b]CrossRef32%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access32%○≥80% are freely accessible
[r]References22 refs✓Minimum 10 references required
[w]Words [REQ]3,330✓Minimum 1,000 words of article body. Current: 3,330
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]25✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 25
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730557
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness17%○≥60% of references from 2025–2026. Current: 17%
[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 (49 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

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 62stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources36%○≥80% from editorially reviewed sources
[t]Trusted68%○≥80% from verified, high-quality sources
[a]DOI45%○≥80% have a Digital Object Identifier
[b]CrossRef32%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access32%○≥80% are freely accessible
[r]References22 refs✓Minimum 10 references required
[w]Words [REQ]3,330✓Minimum 1,000 words of article body. Current: 3,330
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]25✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 25
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730557
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness17%○≥60% of references from 2025–2026. Current: 17%
[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 (49 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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  66stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources33%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef56%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic67%○≥80% from journals/conferences/preprints
[f]Free Access44%○≥80% are freely accessible
[r]References9 refs○Minimum 10 references required
[w]Words [REQ]3,737✓Minimum 1,000 words of article body. Current: 3,737
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]17✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 17
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752830
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness44%○≥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 (4/4 × 30%) + Optional (2/7 × 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 66stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources33%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef56%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic67%○≥80% from journals/conferences/preprints
[f]Free Access44%○≥80% are freely accessible
[r]References9 refs○Minimum 10 references required
[w]Words [REQ]3,737✓Minimum 1,000 words of article body. Current: 3,737
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]17✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 17
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752830
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness44%○≥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 (4/4 × 30%) + Optional (2/7 × 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 by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752832  63stabilfr·wuedophcgmx
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]4,520✓Minimum 1,000 words of article body. Current: 4,520
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]17✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 17
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752832
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness5%○≥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 (4/4 × 30%) + Optional (2/7 × 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 63stabilfr·wuedophcgmx
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]4,520✓Minimum 1,000 words of article body. Current: 4,520
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]17✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 17
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752832
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness5%○≥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 (4/4 × 30%) + Optional (2/7 × 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  54stabilfr·wuedophcgmx
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]4,970✓Minimum 1,000 words of article body. Current: 4,970
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]18✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 18
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18704562
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness9%○≥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 (4/4 × 30%) + Optional (2/7 × 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 54stabilfr·wuedophcgmx
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]4,970✓Minimum 1,000 words of article body. Current: 4,970
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]18✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 18
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18704562
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness9%○≥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 (4/4 × 30%) + Optional (2/7 × 10%)
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Medical ML: Ukrainian Medical Imaging Infrastructure — Current State and AI Readiness Assessment

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752836  52stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]2,387✓Minimum 1,000 words of article body. Current: 2,387
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]8✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 8
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752836
[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 (32 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

Ukraine's medical imaging infrastructure stands at a critical inflection point, shaped by decades of post-Soviet underinvestment, ambitious healthcare reform since 2017, and the devastating impact of the ongoing Russian invasion since February 2022. This comprehensive analysis examines the current state of diagnostic imaging capabilities across Ukrainian healthcare facilities, assessing equipme...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752836 52stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]2,387✓Minimum 1,000 words of article body. Current: 2,387
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]8✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 8
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752836
[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 (32 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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