Data Requirements and Quality Standards for Medical Imaging AI

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752896  45stabilfr·wdophcgmx
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This article examines the critical data quality standards required for medical imaging AI systems, revealing that of 1,016 FDA-approved AI medical devices, 93.3% did not report training data source and 76.3% lacked demographic information. We establish a comprehensive framework for data quality assessment including the six pillars of medical imaging data quality, bias sources and mitigation str...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752896 45stabilfr·wdophcgmx
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Data Requirements and Quality Standards for Medical ML

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752898  36stabilfr·wdophcgmx
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Models pre-trained on a Collection of Public Medical Image Datasets (CPMID) covering X-ray, CT, and MRI outperformed ImageNet pre-training by:

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752898 36stabilfr·wdophcgmx
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ML Model Taxonomy for Medical Imaging

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752900  43stabilfr·wdophcgmx
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Article #4 in "Machine L[REDACTED]g for Medical Diagnosis" Research Series By Oleh Ivchenko, Researcher, ONPU | Stabilarity Hub | February 8, 2026 Questions Addressed: How do CNN, ViT, and hybrid models compare for medical imaging? Which architecture is best for specific modalities?

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752900 43stabilfr·wdophcgmx
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Ukrainian Healthcare System: Current Medical Imaging Practices

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752902  21stabilfr·wdophcgmx
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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 21stabilfr·wdophcgmx
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State of Medical AI Adoption: 1,200 Devices Approved, 81% of Hospitals at Zero

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DOI: 10.5281/zenodo.18752906  40stabilfr·wdophcgmx
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Global medical AI has exploded with 1,200+ FDA-approved devices, yet 81% of US hospitals have no AI adoption. Article #2 maps the adoption paradox, regional variation, success rates by use case, and the critical barriers—with lessons for Ukrainian healthcare.

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DOI: 10.5281/zenodo.18752906 40stabilfr·wdophcgmx
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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 by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752908  43stabilfr·wdophcgmx
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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 43stabilfr·wdophcgmx
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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 by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752910  52stabilfr·wdophcgmx
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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 52stabilfr·wdophcgmx
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Chart comparing AI model training costs from GPT-4 at 00M+ to DeepSeek-R1 at /usr/bin/bash.25M

Cost-Effective AI Development: A Research Review

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DOI: 10.5281/zenodo.18752912  15stabilfr·wdophcgmx
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A comprehensive review of research on cost-effective AI development, examining how organizations achieve state-of-the-art capabilities at 400x lower costs through techniques like RLVR, MoE architectures, and open-weight models.

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DOI: 10.5281/zenodo.18752912 15stabilfr·wdophcgmx
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🚀 StabilarityHub Leads International MedAI Hackathon 2025: Transforming Healthcare with AI

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DOI: 10.5281/zenodo.18752914  22stabilfr·wdophcgmx
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[a]DOI13%○≥80% have a Digital Object Identifier
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[r]References8 refs○Minimum 10 references required
[w]Words [REQ]893✗Minimum 2,000 words for a full research article. Current: 893
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (16 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)

Celebrating the International MedAI Hackathon 2025 — where 50+ innovators from Ukraine, Germany and beyond collaborated to build transformative AI solutions in radiology, mental health, and healthcare operations. Led by StabilarityHub with ONPU, GROMUS, Innova Clinics, and ScanLab. Discover the winning projects and the future of healthcare technology.

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DOI: 10.5281/zenodo.18752914 22stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted13%○≥80% from verified, high-quality sources
[a]DOI13%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed13%○≥80% have metadata indexed
[l]Academic13%○≥80% from journals/conferences/preprints
[f]Free Access38%○≥80% are freely accessible
[r]References8 refs○Minimum 10 references required
[w]Words [REQ]893✗Minimum 2,000 words for a full research article. Current: 893
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752914
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]17%✗≥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
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (16 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)
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2025 AI Research Impact: A Year of Transformation

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DOI: 10.5281/zenodo.18752916  32stabilfr·wdophcgmx
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[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI17%○≥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 Access83%✓≥80% are freely accessible
[r]References6 refs○Minimum 10 references required
[w]Words [REQ]1,630✗Minimum 2,000 words for a full research article. Current: 1,630
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752916
[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 (29 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

2025 marked a fundamental shift in artificial intelligence research—transitioning from "powerful tool" to "fundamental infrastructure." This comprehensive review examines the year's transformative achievements across model efficiency, reasoning capabilities, multimodal intelligence, and real-world deployment. We analyze key breakthroughs including the evolution of the Gemini model series, the e...

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DOI: 10.5281/zenodo.18752916 32stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI17%○≥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 Access83%✓≥80% are freely accessible
[r]References6 refs○Minimum 10 references required
[w]Words [REQ]1,630✗Minimum 2,000 words for a full research article. Current: 1,630
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752916
[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 (29 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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