[Medical ML] Federated Learning for Privacy-Preserving Medical AI Training: Multi-Institutional Collaboration Without Data Sharing

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18685263  78stabilfr·wuedophcgmx
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Federated learning (FL) represents a paradigm shift in collaborative machine learning that enables multiple healthcare institutions to jointly train diagnostic AI models without sharing sensitive patient data. This comprehensive analysis examines the technical foundations, implementation strategies, and real-world deployments of federated learning in medical imaging, addressing the fundamental ...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18685263 78stabilfr·wuedophcgmx
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[s]Reviewed Sources64%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI86%✓≥80% have a Digital Object Identifier
[b]CrossRef64%○≥80% indexed in CrossRef
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[l]Academic86%✓≥80% from journals/conferences/preprints
[f]Free Access43%○≥80% are freely accessible
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Score = Ref Trust (76 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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[Medical ML] Transfer Learning and Domain Adaptation: Bridging the Data Gap in Medical Imaging AI

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18672185  76stabilfr·wuedophcgmx
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[a]DOI86%✓≥80% have a Digital Object Identifier
[b]CrossRef71%○≥80% indexed in CrossRef
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Academic Citation: Ivchenko, O. (2026). Transfer Learning and Domain Adaptation: Bridging the Data Gap in Medical Imaging AI. Medical ML Diagnosis Series. Odesa National Polytechnic University. DOI: 10.5281/zenodo.18672185 Abstract The remarkable success of deep learning in medical imaging has been tempered by a fundamental challenge: the scarcity of large-scale, annotated medical datasets esse...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18672185 76stabilfr·wuedophcgmx
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[s]Reviewed Sources52%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI86%✓≥80% have a Digital Object Identifier
[b]CrossRef71%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic86%✓≥80% from journals/conferences/preprints
[f]Free Access38%○≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]3,162✓Minimum 1,000 words of article body. Current: 3,162
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[h]Freshness14%○≥60% of references from 2025–2026. Current: 14%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (72 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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[Medical ML] Explainable AI (XAI) for Clinical Trust: Bridging the Black Box Gap

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752850  77stabilfr·wuedophcgmx
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[t]Trusted88%✓≥80% from verified, high-quality sources
[a]DOI88%✓≥80% have a Digital Object Identifier
[b]CrossRef56%○≥80% indexed in CrossRef
[i]Indexed6%○≥80% have metadata indexed
[l]Academic88%✓≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]4,862✓Minimum 1,000 words of article body. Current: 4,862
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Score = Ref Trust (73 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

of radiologists report they would not trust an AI diagnosis they cannot understand, even if the system demonstrates superior accuracy

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752850 77stabilfr·wuedophcgmx
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[t]Trusted88%✓≥80% from verified, high-quality sources
[a]DOI88%✓≥80% have a Digital Object Identifier
[b]CrossRef56%○≥80% indexed in CrossRef
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[l]Academic88%✓≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
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Score = Ref Trust (73 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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[Medical ML] Hybrid Models: Best of Both Worlds — CNN-Transformer Architectures for Clinical Imaging

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752852  52stabilfr·wuedophcgmx
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[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
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[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
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Score = Ref Trust (32 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

The convergence of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) represents a paradigm shift in medical image analysis, addressing the fundamental limitations of each architecture through strategic integration. This comprehensive review examines hybrid CNN-Transformer architectures that leverage CNNs' exceptional local feature extraction capabilities alongside Transformers...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752852 52stabilfr·wuedophcgmx
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[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
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[w]Words [REQ]3,672✓Minimum 1,000 words of article body. Current: 3,672
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[o]ORCID✓✓Author ORCID verified for academic identity
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Score = Ref Trust (32 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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[Medical ML] Vision Transformers in Radiology: Architecture, Applications, and Clinical Performance

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18672181  52stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
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[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]4,219✓Minimum 1,000 words of article body. Current: 4,219
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18672181
[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]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%)

Vision Transformers (ViT) represent a paradigm shift in medical image analysis, applying the revolutionary attention mechanism from natural language processing to radiological imaging. This comprehensive review examines the theoretical foundations, architectural innovations, and clinical applications of Vision Transformers across radiology subspecialties including chest radiography, computed to...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18672181 52stabilfr·wuedophcgmx
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[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]4,219✓Minimum 1,000 words of article body. Current: 4,219
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18672181
[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
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[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] Physician Resistance: Causes and Solutions

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752854  77stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
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[t]Trusted88%✓≥80% from verified, high-quality sources
[a]DOI88%✓≥80% have a Digital Object Identifier
[b]CrossRef76%○≥80% indexed in CrossRef
[i]Indexed12%○≥80% have metadata indexed
[l]Academic76%○≥80% from journals/conferences/preprints
[f]Free Access18%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]3,441✓Minimum 1,000 words of article body. Current: 3,441
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]15✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 15
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752854
[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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (73 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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 77stabilfr·wuedophcgmx
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[s]Reviewed Sources41%○≥80% from editorially reviewed sources
[t]Trusted88%✓≥80% from verified, high-quality sources
[a]DOI88%✓≥80% have a Digital Object Identifier
[b]CrossRef76%○≥80% indexed in CrossRef
[i]Indexed12%○≥80% have metadata indexed
[l]Academic76%○≥80% from journals/conferences/preprints
[f]Free Access18%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]3,441✓Minimum 1,000 words of article body. Current: 3,441
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]15✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 15
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752854
[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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (73 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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[Medical ML] Failed Implementations: What Went Wrong

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752858  77stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources27%○≥80% from editorially reviewed sources
[t]Trusted87%✓≥80% from verified, high-quality sources
[a]DOI87%✓≥80% have a Digital Object Identifier
[b]CrossRef80%✓≥80% indexed in CrossRef
[i]Indexed13%○≥80% have metadata indexed
[l]Academic87%✓≥80% from journals/conferences/preprints
[f]Free Access40%○≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]3,482✓Minimum 1,000 words of article body. Current: 3,482
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]12✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 12
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752858
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness6%○≥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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (74 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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 77stabilfr·wuedophcgmx
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[s]Reviewed Sources27%○≥80% from editorially reviewed sources
[t]Trusted87%✓≥80% from verified, high-quality sources
[a]DOI87%✓≥80% have a Digital Object Identifier
[b]CrossRef80%✓≥80% indexed in CrossRef
[i]Indexed13%○≥80% have metadata indexed
[l]Academic87%✓≥80% from journals/conferences/preprints
[f]Free Access40%○≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]3,482✓Minimum 1,000 words of article body. Current: 3,482
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]12✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 12
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752858
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness6%○≥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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (74 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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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.18752860  74stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources42%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI83%✓≥80% have a Digital Object Identifier
[b]CrossRef25%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
[l]Academic83%✓≥80% from journals/conferences/preprints
[f]Free Access42%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]3,352✓Minimum 1,000 words of article body. Current: 3,352
[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.18752860
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness7%○≥60% of references from 2025–2026. Current: 7%
[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 (68 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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 74stabilfr·wuedophcgmx
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[s]Reviewed Sources42%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI83%✓≥80% have a Digital Object Identifier
[b]CrossRef25%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
[l]Academic83%✓≥80% from journals/conferences/preprints
[f]Free Access42%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]3,352✓Minimum 1,000 words of article body. Current: 3,352
[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.18752860
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness7%○≥60% of references from 2025–2026. Current: 7%
[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 (68 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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  74stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources60%○≥80% from editorially reviewed sources
[t]Trusted80%✓≥80% from verified, high-quality sources
[a]DOI70%○≥80% have a Digital Object Identifier
[b]CrossRef70%○≥80% indexed in CrossRef
[i]Indexed20%○≥80% have metadata indexed
[l]Academic70%○≥80% from journals/conferences/preprints
[f]Free Access20%○≥80% are freely accessible
[r]References10 refs✓Minimum 10 references required
[w]Words [REQ]3,254✓Minimum 1,000 words of article body. Current: 3,254
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]9✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 9
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752862
[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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (68 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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 74stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources60%○≥80% from editorially reviewed sources
[t]Trusted80%✓≥80% from verified, high-quality sources
[a]DOI70%○≥80% have a Digital Object Identifier
[b]CrossRef70%○≥80% indexed in CrossRef
[i]Indexed20%○≥80% have metadata indexed
[l]Academic70%○≥80% from journals/conferences/preprints
[f]Free Access20%○≥80% are freely accessible
[r]References10 refs✓Minimum 10 references required
[w]Words [REQ]3,254✓Minimum 1,000 words of article body. Current: 3,254
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]9✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 9
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752862
[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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (68 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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  75stabilfr·wuedophcgmx
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[s]Reviewed Sources45%○≥80% from editorially reviewed sources
[t]Trusted82%✓≥80% from verified, high-quality sources
[a]DOI82%✓≥80% have a Digital Object Identifier
[b]CrossRef45%○≥80% indexed in CrossRef
[i]Indexed18%○≥80% have metadata indexed
[l]Academic82%✓≥80% from journals/conferences/preprints
[f]Free Access45%○≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]2,705✓Minimum 1,000 words of article body. Current: 2,705
[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.18695004
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness8%○≥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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (71 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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 75stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources45%○≥80% from editorially reviewed sources
[t]Trusted82%✓≥80% from verified, high-quality sources
[a]DOI82%✓≥80% have a Digital Object Identifier
[b]CrossRef45%○≥80% indexed in CrossRef
[i]Indexed18%○≥80% have metadata indexed
[l]Academic82%✓≥80% from journals/conferences/preprints
[f]Free Access45%○≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]2,705✓Minimum 1,000 words of article body. Current: 2,705
[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.18695004
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness8%○≥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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (71 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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