Category: Medical ML Diagnosis

ML for Medical Imaging Diagnosis

Ukrainian Healthcare System: Current Medical Imaging Practices

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752902  

Ukraine's healthcare system represents a unique case study in digital transformation under extraordinary circumstances. The two-level electronic healthcare system (EHS), with 36 million registered patients and 1.6 billion electronic medical records, provides a robust foundation for AI integration—despite wartime challenges that reduced viable Medical Information System (MIS) providers from 40 t...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752902
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6-phase research framework for ML-augmented medical diagnosis

ML for Medical Diagnosis: Research Goals and Framework for Ukrainian Healthcare

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752908  49stabilfr·wdophcgmx
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Score = Ref Trust (57 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Launching a 12-week research program to build a practical framework for ML-augmented medical image diagnosis in Ukrainian healthcare. Article #1 establishes methodology, introduces Stabilarity Hub ecosystem, and outlines the path from research to ScanLab implementation.

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752908 49stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources44%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI44%○≥80% have a Digital Object Identifier
[b]CrossRef22%○≥80% indexed in CrossRef
[i]Indexed44%○≥80% have metadata indexed
[l]Academic56%○≥80% from journals/conferences/preprints
[f]Free Access22%○≥80% are freely accessible
[r]References9 refs○Minimum 10 references required
[w]Words [REQ]876✗Minimum 2,000 words for a full research article. Current: 876
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752908
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (57 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Diagram showing how ML integrates into medical image analysis workflow from acquisition to diagnosis

Image Classification and ML in Disease Recognition: A Research Review

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752910  66stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
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[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI80%✓≥80% have a Digital Object Identifier
[b]CrossRef40%○≥80% indexed in CrossRef
[i]Indexed80%✓≥80% have metadata indexed
[l]Academic80%✓≥80% from journals/conferences/preprints
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[r]References5 refs○Minimum 10 references required
[w]Words [REQ]1,976✗Minimum 2,000 words for a full research article. Current: 1,976
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752910
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
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[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 (86 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

A comprehensive review of machine learning in medical image analysis, examining which ML techniques apply at each diagnostic stage, evidence-based best practices for doctor-AI collaboration, and unique conclusions on reducing diagnostic errors.

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752910 66stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources60%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI80%✓≥80% have a Digital Object Identifier
[b]CrossRef40%○≥80% indexed in CrossRef
[i]Indexed80%✓≥80% have metadata indexed
[l]Academic80%✓≥80% from journals/conferences/preprints
[f]Free Access40%○≥80% are freely accessible
[r]References5 refs○Minimum 10 references required
[w]Words [REQ]1,976✗Minimum 2,000 words for a full research article. Current: 1,976
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752910
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥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 (86 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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