Medical ML: Ukrainian Medical Imaging Infrastructure — Current State and AI Readiness Assessment

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752836  40stabilfr·wdophcgmx
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Score = Ref Trust (32 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Ukraine's medical imaging infrastructure stands at a critical inflection point, shaped by decades of post-Soviet underinvestment, ambitious healthcare reform since 2017, and the devastating impact of the ongoing Russian invasion since February 2022. This comprehensive analysis examines the current state of diagnostic imaging capabilities across Ukrainian healthcare facilities, assessing equipme...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752836 40stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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Score = Ref Trust (32 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Medical ML: Training Programs for Physicians — Building AI Competency in Medical Imaging

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752838  30stabilfr·wdophcgmx
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Score = Ref Trust (15 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The successful integration of artificial intelligence into clinical radiology practice hinges upon physicians' comprehensive understanding of AI principles, capabilities, and limitations. This research article examines the current landscape of physician training programs for AI in medical imaging, analyzing curriculum frameworks, competency standards, and pedagogical approaches across internati...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752838 30stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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Score = Ref Trust (15 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Medical ML: Quality Assurance and Monitoring for Medical AI Systems

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18709914  58stabilfr·wdophcgmx
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[t]Trusted81%✓≥80% from verified, high-quality sources
[a]DOI63%○≥80% have a Digital Object Identifier
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[l]Academic63%○≥80% from journals/conferences/preprints
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]38%✗≥60% of references from 2025–2026. Current: 38%
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[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (62 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The deployment of machine l[REDACTED]g algorithms in clinical diagnostics represents one of healthcare's most significant technological advances. However, unlike traditional medical devices, AI systems are uniquely susceptible to performance degradation through data drift, concept shift, and environmental changes that can compromise patient safety. This article presents a comprehensive framewor...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18709914 58stabilfr·wdophcgmx
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[t]Trusted81%✓≥80% from verified, high-quality sources
[a]DOI63%○≥80% have a Digital Object Identifier
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[l]Academic63%○≥80% from journals/conferences/preprints
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[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]3,430✓Minimum 2,000 words for a full research article. Current: 3,430
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]38%✗≥60% of references from 2025–2026. Current: 38%
[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 (62 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Marketing AI: Introduction – The AI Revolution in Marketing

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DOI: 10.5281/zenodo.18752843  40stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥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 (3/5 × 30%) + Optional (1/4 × 10%)

The integration of artificial intelligence into marketing represents one of the most significant transformations in the history of commercial communication. This foundational article examines the evolution, current state, and future trajectory of AI in marketing, establishing a comprehensive framework for understanding this technological revolution. Drawing upon extensive industry research, aca...

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DOI: 10.5281/zenodo.18752843 40stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Medical ML: Confidence Thresholds and Escalation Protocols in Clinical AI Deployment

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752845  40stabilfr·wdophcgmx
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[a]DOI33%○≥80% have a Digital Object Identifier
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
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Score = Ref Trust (32 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The deployment of artificial intelligence in medical imaging requires sophisticated mechanisms for determining when AI predictions should be trusted autonomously versus when human expert review is mandatory. This article presents a comprehensive framework for implementing confidence thresholds and escalation protocols in clinical AI systems, addressing the critical gap between algorithmic outpu...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752845 40stabilfr·wdophcgmx
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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]3,906✓Minimum 2,000 words for a full research article. Current: 3,906
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥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 (3/5 × 30%) + Optional (1/4 × 10%)
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Medical ML: Radiologist-AI Collaboration Protocols – Designing Human-Machine Partnerships for Clinical Excellence

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18704558  40stabilfr·wdophcgmx
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[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
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[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
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[w]Words [REQ]3,783✓Minimum 2,000 words for a full research article. Current: 3,783
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18704558
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The integration of artificial intelligence into radiology practice represents more than a technological upgrade—it constitutes a fundamental reimagining of diagnostic workflows that have remained largely unchanged for decades. This article examines the critical protocols governing radiologist-AI collaboration, analyzing the spectrum of interaction models from autonomous AI triage to fully super...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18704558 40stabilfr·wdophcgmx
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[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
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[l]Academic33%○≥80% from journals/conferences/preprints
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[w]Words [REQ]3,783✓Minimum 2,000 words for a full research article. Current: 3,783
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18704558
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥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]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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[Medical ML] PACS Integration Strategies for AI-Powered Medical Imaging: A Comprehensive Framework

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752847  60stabilfr·wdophcgmx
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[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]3,487✓Minimum 2,000 words for a full research article. Current: 3,487
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752847
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]16%✗≥60% of references from 2025–2026. Current: 16%
[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 (65 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 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 60stabilfr·wdophcgmx
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[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]3,487✓Minimum 2,000 words for a full research article. Current: 3,487
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752847
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]16%✗≥60% of references from 2025–2026. Current: 16%
[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 (65 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 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  66stabilfr·wdophcgmx
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]2,961✓Minimum 2,000 words for a full research article. Current: 2,961
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18685263
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]7%✗≥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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (76 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Federated l[REDACTED]g (FL) represents a paradigm shift in collaborative machine l[REDACTED]g 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 l[REDACTED]g in medical imaging, addressing the ...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18685263 66stabilfr·wdophcgmx
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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
[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]2,961✓Minimum 2,000 words for a full research article. Current: 2,961
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18685263
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]7%✗≥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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (76 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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[Medical ML] Transfer Learning and Domain Adaptation: Bridging the Data Gap in Medical Imaging AI

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18672185  64stabilfr·wdophcgmx
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]3,356✓Minimum 2,000 words for a full research article. Current: 3,356
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Score = Ref Trust (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Academic Citation: Ivchenko, O. (2026). Transfer L[REDACTED]g 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 l[REDACTED]g in medical imaging has been tempered by a fundamental challenge: the scarcity of large-scale, annotated medical datas...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18672185 64stabilfr·wdophcgmx
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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]3,356✓Minimum 2,000 words for a full research article. Current: 3,356
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18672185
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]14%✗≥60% of references from 2025–2026. Current: 14%
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Score = Ref Trust (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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[Medical ML] Explainable AI (XAI) for Clinical Trust: Bridging the Black Box Gap

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752850  64stabilfr·wdophcgmx
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752850
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Score = Ref Trust (73 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 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 64stabilfr·wdophcgmx
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]4,881✓Minimum 2,000 words for a full research article. Current: 4,881
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752850
[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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (73 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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