Author: Yoman

Medical ML: Cost-Benefit Analysis of AI Implementation for Ukrainian Hospitals

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752830  66stabilfr·wuedophcgmx
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Score = Ref Trust (56 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

The adoption of artificial intelligence in medical imaging presents Ukrainian healthcare institutions with a complex economic decision. This article provides a comprehensive cost-benefit analysis framework specifically designed for the Ukrainian healthcare context, accounting for the country's unique economic conditions, wartime constraints, and institutional structures. We examine the total co...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752830 66stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources33%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef56%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic67%○≥80% from journals/conferences/preprints
[f]Free Access44%○≥80% are freely accessible
[r]References9 refs○Minimum 10 references required
[w]Words [REQ]3,735✓Minimum 1,000 words of article body. Current: 3,735
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]17✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 17
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752830
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness44%○≥60% of references from 2025–2026. Current: 44%
[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 (56 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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[Medical ML] PACS Integration Strategies for AI-Powered Medical Imaging: A Comprehensive Framework

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752847  72stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources37%○≥80% from editorially reviewed sources
[t]Trusted79%○≥80% from verified, high-quality sources
[a]DOI79%○≥80% have a Digital Object Identifier
[b]CrossRef37%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic79%○≥80% from journals/conferences/preprints
[f]Free Access32%○≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]3,488✓Minimum 1,000 words of article body. Current: 3,488
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[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
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Score = Ref Trust (65 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

The integration of artificial intelligence (AI) algorithms into Picture Archiving and Communication Systems (PACS) represents a pivotal transformation in diagnostic radiology, enabling automated analysis, enhanced detection, and improved workflow efficiency. This comprehensive review examines the technical architectures, implementation strategies, and organizational considerations essential for...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752847 72stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources37%○≥80% from editorially reviewed sources
[t]Trusted79%○≥80% from verified, high-quality sources
[a]DOI79%○≥80% have a Digital Object Identifier
[b]CrossRef37%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic79%○≥80% from journals/conferences/preprints
[f]Free Access32%○≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]3,488✓Minimum 1,000 words of article body. Current: 3,488
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]16✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 16
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752847
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
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Score = Ref Trust (65 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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[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
BadgeMetricValueStatusDescription
[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
[i]Indexed21%○≥80% have metadata indexed
[l]Academic86%✓≥80% from journals/conferences/preprints
[f]Free Access43%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]2,871✓Minimum 1,000 words of article body. Current: 2,871
[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.18685263
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
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Score = Ref Trust (76 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

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
BadgeMetricValueStatusDescription
[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
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]2,871✓Minimum 1,000 words of article body. Current: 2,871
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]11✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 11
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18685263
[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
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[x]Cited by0○Referenced by 0 other hub article(s)
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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[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
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[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
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Score = Ref Trust (72 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

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
BadgeMetricValueStatusDescription
[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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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18672185
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness14%○≥60% of references from 2025–2026. Current: 14%
[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 (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
BadgeMetricValueStatusDescription
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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
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]18✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 18
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752850
[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
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[x]Cited by0○Referenced by 0 other hub article(s)
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
BadgeMetricValueStatusDescription
[s]Reviewed Sources44%○≥80% from editorially reviewed sources
[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
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]18✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 18
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752850
[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 (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

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752852  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]3,672✓Minimum 1,000 words of article body. Current: 3,672
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]16✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 16
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752852
[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%)

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
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]3,672✓Minimum 1,000 words of article body. Current: 3,672
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]16✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 16
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752852
[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%)
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[Medical ML] Vision Transformers in Radiology: Architecture, Applications, and Clinical Performance

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18672181  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]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
[e]Evidence [REQ]37✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 37
[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
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]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
[e]Evidence [REQ]37✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 37
[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%)
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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
[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%)

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
BadgeMetricValueStatusDescription
[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

Posted on by Yoman
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
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%)
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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  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
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%)
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