[Medical ML] Hybrid Models: Best of Both Worlds

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752864  73stabilfr·wuedophcgmx
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Hybrid architectures that combine convolutional neural networks (CNNs) with transformer-based modules are rapidly becoming the pragmatic choice for medical imaging tasks. They balance CNNs' efficiency and inductive biases with transformers' long-range context modeling. This article summarizes the state of hybrid models, evaluation results, and deployment recommendations for Ukrainian healthcare...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752864 73stabilfr·wuedophcgmx
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[s]Reviewed Sources11%○≥80% from editorially reviewed sources
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[Medical ML] CNN Architectures for Medical Imaging: From ResNet to EfficientNet

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752870  52stabilfr·wuedophcgmx
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Academic Citation: Ivchenko, O. (2026). [Medical ML] CNN Architectures for Medical Imaging: From ResNet to EfficientNet. Medical Machine Learning for Diagnosis Series. Odesa National Polytechnic University. DOI: 10.5281/zenodo.14963752 *By Oleh Ivchenko | February 8, 2026* Convolutional Neural Networks (CNNs) have fundamentally transformed medical image analysis, evolving from simple feature ex...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752870 52stabilfr·wuedophcgmx
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[Medical ML] Physician Resistance: Causes and Solutions

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.23486180  79stabilfr·wuedophcgmx
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The integration of artificial intelligence into clinical practice faces a critical bottleneck: physician resistance. Despite over $66 billion invested globally in healthcare AI, adoption remains stubbornly low. This article examines the multifaceted causes of physician resistance—spanning professional identity threats, liability concerns, and workflow disruption—and presents evidence-based stra...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.23486180 79stabilfr·wuedophcgmx
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[Medical ML] China’s Massive Medical AI Deployment

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18645077  68stabilfr·wuedophcgmx
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China has emerged as the world's fastest-growing healthcare AI market, demonstrating that large-scale medical AI deployment is achievable through coordinated policy, infrastructure investment, and strategic regulatory frameworks. This article provides comprehensive analysis of China's medical AI ecosystem, examining market growth from $900 million in 2020 to a projected $18.9 billion by 2030, t...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18645077 68stabilfr·wuedophcgmx
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Data Requirements and Quality Standards for Medical Imaging AI

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752896  57stabilfr·wuedophcgmx
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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 57stabilfr·wuedophcgmx
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[a]DOI8%○≥80% have a Digital Object Identifier
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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  48stabilfr·wuedophcgmx
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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 48stabilfr·wuedophcgmx
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ML Model Taxonomy for Medical Imaging

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752900  63stabilfr·wuedophcgmx
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[t]Trusted80%✓≥80% from verified, high-quality sources
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[w]Words [REQ]1,038✓Minimum 1,000 words of article body. Current: 1,038
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Article #4 in "Machine Learning 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 63stabilfr·wuedophcgmx
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[a]DOI20%○≥80% have a Digital Object Identifier
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[f]Free Access100%✓≥80% are freely accessible
[r]References10 refs✓Minimum 10 references required
[w]Words [REQ]1,038✓Minimum 1,000 words of article body. Current: 1,038
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Score = Ref Trust (50 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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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  49stabilfr·wuedophcgmx
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[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI25%○≥80% have a Digital Object Identifier
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[f]Free Access100%✓≥80% are freely accessible
[r]References4 refs○Minimum 10 references required
[w]Words [REQ]2,636✓Minimum 1,000 words of article body. Current: 2,636
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[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.18752902
[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 (27 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

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 49stabilfr·wuedophcgmx
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[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]2,636✓Minimum 1,000 words of article body. Current: 2,636
[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.18752902
[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 (27 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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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  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]2,794✓Minimum 1,000 words of article body. Current: 2,794
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]10✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 10
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752906
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness25%○≥60% of references from 2025–2026. Current: 25%
[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 (32 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

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 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]2,794✓Minimum 1,000 words of article body. Current: 2,794
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]10✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 10
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752906
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness25%○≥60% of references from 2025–2026. Current: 25%
[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 (32 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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  71stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources43%○≥80% from editorially reviewed sources
[t]Trusted71%○≥80% from verified, high-quality sources
[a]DOI57%○≥80% have a Digital Object Identifier
[b]CrossRef29%○≥80% indexed in CrossRef
[i]Indexed57%○≥80% have metadata indexed
[l]Academic57%○≥80% from journals/conferences/preprints
[f]Free Access57%○≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]1,960✓Minimum 1,000 words of article body. Current: 1,960
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]7✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 7
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752910
[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 (63 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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 71stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources43%○≥80% from editorially reviewed sources
[t]Trusted71%○≥80% from verified, high-quality sources
[a]DOI57%○≥80% have a Digital Object Identifier
[b]CrossRef29%○≥80% indexed in CrossRef
[i]Indexed57%○≥80% have metadata indexed
[l]Academic57%○≥80% from journals/conferences/preprints
[f]Free Access57%○≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]1,960✓Minimum 1,000 words of article body. Current: 1,960
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]7✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 7
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752910
[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 (63 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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