Category: Medical ML Diagnosis

ML for Medical Imaging Diagnosis

[Medical ML] Physician Resistance: Causes and Solutions

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752854  70stabilfr·wdophcgmx
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[s]Reviewed Sources47%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef87%✓≥80% indexed in CrossRef
[i]Indexed13%○≥80% have metadata indexed
[l]Academic87%✓≥80% from journals/conferences/preprints
[f]Free Access7%○≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]3,483✓Minimum 2,000 words for a full research article. Current: 3,483
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752854
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]6%✗≥60% 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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (82 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Despite compelling evidence of artificial intelligence's potential to enhance diagnostic accuracy and clinical efficiency, physician adoption of AI tools remains inconsistent and frequently falls short of implementation expectations. This comprehensive analysis examines the multidimensional phenomenon of physician resistance to healthcare AI, moving beyond simplistic narratives of technophobia ...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752854 70stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources47%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef87%✓≥80% indexed in CrossRef
[i]Indexed13%○≥80% have metadata indexed
[l]Academic87%✓≥80% from journals/conferences/preprints
[f]Free Access7%○≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]3,483✓Minimum 2,000 words for a full research article. Current: 3,483
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752854
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]6%✗≥60% 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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (82 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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[Medical ML] Failed Implementations: What Went Wrong

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752858  71stabilfr·wdophcgmx
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[s]Reviewed Sources31%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef92%✓≥80% indexed in CrossRef
[i]Indexed15%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access31%○≥80% are freely accessible
[r]References13 refs✓Minimum 10 references required
[w]Words [REQ]3,533✓Minimum 2,000 words for a full research article. Current: 3,533
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752858
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]8%✗≥60% of references from 2025–2026. Current: 8%
[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 (84 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The healthcare artificial intelligence literature predominantly features success stories, creating a survivorship bias that inadequately prepares implementers for the challenges of real-world deployment. This paper addresses this gap through systematic analysis of documented healthcare AI implementation failures, examining projects that failed to achieve their objectives, were abandoned after d...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752858 71stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources31%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef92%✓≥80% indexed in CrossRef
[i]Indexed15%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access31%○≥80% are freely accessible
[r]References13 refs✓Minimum 10 references required
[w]Words [REQ]3,533✓Minimum 2,000 words for a full research article. Current: 3,533
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752858
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]8%✗≥60% of references from 2025–2026. Current: 8%
[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 (84 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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[Medical ML] China’s Massive Medical AI Deployment

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752860  69stabilfr·wdophcgmx
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[s]Reviewed Sources50%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef30%○≥80% indexed in CrossRef
[i]Indexed10%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access30%○≥80% are freely accessible
[r]References10 refs✓Minimum 10 references required
[w]Words [REQ]3,324✓Minimum 2,000 words for a full research article. Current: 3,324
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752860
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]9%✗≥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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (81 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

China has emerged as the global leader in medical artificial intelligence deployment, with AI-powered diagnostic systems operational in over 30,000 hospitals serving a population of 1.4 billion people. This comprehensive analysis examines the strategic, technical, and organizational dimensions of China's unprecedented healthcare AI expansion, drawing on regulatory filings, published research, i...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752860 69stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources50%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef30%○≥80% indexed in CrossRef
[i]Indexed10%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access30%○≥80% are freely accessible
[r]References10 refs✓Minimum 10 references required
[w]Words [REQ]3,324✓Minimum 2,000 words for a full research article. Current: 3,324
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752860
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]9%✗≥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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (81 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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[Medical ML] UK NHS AI Lab: Lessons Learned from £250M Programme

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752862  70stabilfr·wdophcgmx
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[s]Reviewed Sources75%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI88%✓≥80% have a Digital Object Identifier
[b]CrossRef88%✓≥80% indexed in CrossRef
[i]Indexed25%○≥80% have metadata indexed
[l]Academic88%✓≥80% from journals/conferences/preprints
[f]Free Access0%○≥80% are freely accessible
[r]References8 refs○Minimum 10 references required
[w]Words [REQ]3,282✓Minimum 2,000 words for a full research article. Current: 3,282
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752862
[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 (82 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The United Kingdom's National Health Service AI Lab, established in 2019 with a £250 million investment, represents one of the most ambitious national initiatives to accelerate artificial intelligence adoption in public healthcare. This comprehensive analysis examines the programme's evolution, achievements, challenges, and transferable lessons over its five-year operational history. Through sy...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752862 70stabilfr·wdophcgmx
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[s]Reviewed Sources75%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI88%✓≥80% have a Digital Object Identifier
[b]CrossRef88%✓≥80% indexed in CrossRef
[i]Indexed25%○≥80% have metadata indexed
[l]Academic88%✓≥80% from journals/conferences/preprints
[f]Free Access0%○≥80% are freely accessible
[r]References8 refs○Minimum 10 references required
[w]Words [REQ]3,282✓Minimum 2,000 words for a full research article. Current: 3,282
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752862
[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 (82 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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[Medical ML] EU Experience: CE-Marked Diagnostic AI

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18695004  72stabilfr·wdophcgmx
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[s]Reviewed Sources63%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef63%○≥80% indexed in CrossRef
[i]Indexed25%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access25%○≥80% are freely accessible
[r]References8 refs○Minimum 10 references required
[w]Words [REQ]2,713✓Minimum 2,000 words for a full research article. Current: 2,713
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18695004
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]10%✗≥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 (85 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The European Union has emerged as a global leader in establishing comprehensive regulatory frameworks for artificial intelligence in medical diagnostics, with the CE marking process serving as the cornerstone of quality assurance and patient safety. This paper presents an extensive analysis of the EU's experience with CE-marked diagnostic AI systems, examining the regulatory journey from the Me...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18695004 72stabilfr·wdophcgmx
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[s]Reviewed Sources63%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef63%○≥80% indexed in CrossRef
[i]Indexed25%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access25%○≥80% are freely accessible
[r]References8 refs○Minimum 10 references required
[w]Words [REQ]2,713✓Minimum 2,000 words for a full research article. Current: 2,713
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18695004
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]10%✗≥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 (85 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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[Medical ML] Hybrid Models: Best of Both Worlds

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752864  65stabilfr·wdophcgmx
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[s]Reviewed Sources14%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef14%○≥80% indexed in CrossRef
[i]Indexed43%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]1,879✗Minimum 2,000 words for a full research article. Current: 1,879
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752864
[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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (84 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

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 65stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources14%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef14%○≥80% indexed in CrossRef
[i]Indexed43%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]1,879✗Minimum 2,000 words for a full research article. Current: 1,879
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752864
[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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (84 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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[Medical ML] Vision Transformers in Radiology: From Image Patches to Clinical Decisions

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752868  75stabilfr·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]Indexed100%✓≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References1 refs○Minimum 10 references required
[w]Words [REQ]1,427✗Minimum 2,000 words for a full research article. Current: 1,427
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752868
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[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 (91 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Vision Transformers (ViTs) have emerged as a transformative architecture in medical imaging, challenging the decade-long dominance of Convolutional Neural Networks (CNNs). Unlike CNNs that build understanding through hierarchical local feature extraction, ViTs treat images as sequences of patches and leverage self-attention mechanisms to capture global context from the first layer. This compreh...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752868 75stabilfr·wdophcgmx
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[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]Indexed100%✓≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References1 refs○Minimum 10 references required
[w]Words [REQ]1,427✗Minimum 2,000 words for a full research article. Current: 1,427
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752868
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[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 (91 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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[Medical ML] CNN Architectures for Medical Imaging: From ResNet to EfficientNet

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752870  75stabilfr·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]Indexed100%✓≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References1 refs○Minimum 10 references required
[w]Words [REQ]1,518✗Minimum 2,000 words for a full research article. Current: 1,518
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752870
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[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 (91 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

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 75stabilfr·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]Indexed100%✓≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References1 refs○Minimum 10 references required
[w]Words [REQ]1,518✗Minimum 2,000 words for a full research article. Current: 1,518
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752870
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[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 (91 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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[Medical ML] Physician Resistance: Causes and Solutions

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752874  69stabilfr·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
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[i]Indexed100%✓≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References1 refs○Minimum 10 references required
[w]Words [REQ]1,614✗Minimum 2,000 words for a full research article. Current: 1,614
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752874
[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]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (91 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

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.18752874 69stabilfr·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]Indexed100%✓≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References1 refs○Minimum 10 references required
[w]Words [REQ]1,614✗Minimum 2,000 words for a full research article. Current: 1,614
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752874
[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]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (91 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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[Medical ML] Failed Implementations: What Went Wrong

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752878  

Despite over $66.8 billion invested globally in healthcare AI (2021 alone), the field has produced spectacular failures alongside its successes. Understanding what went wrong—and why—is essential for any hospital considering AI adoption.

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752878
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