Medical ML Diagnosis

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Medical imaging research — MRI scan analysis
Research Series
DOI 10.5281/zenodo.18752910
Machine Learning for Medical Imaging Diagnosis in Ukrainian Healthcare

Oleh Ivchenko1

1 Odesa National Polytechnic University (ONPU)

Type
Academic Research Series
Status
Complete · 43 articles · 2025–2026
Tool
ScanLab  →  API  →  GitHub
43 Articles  ·  6 Research Phases  ·  2025–2026  ·  Complete
Abstract

Ukrainian healthcare faces a structural challenge: demand for accurate, fast, and accessible medical imaging diagnostics consistently outpaces the supply of trained specialists, particularly in regions outside major urban centres. This research series presents a complete, reproducible evidence base for ML-assisted medical imaging diagnosis in Ukrainian healthcare — from first principles through clinical-readiness criteria. Across 43 articles and six thematic phases, it surveys deep learning architectures, establishes explainability as a first-class requirement, documents Ukrainian healthcare infrastructure constraints, and delivers ScanLab: an open diagnostic tool implementing the series’ architectural recommendations. The work addresses a gap in the literature dominated by high-resource settings, providing structured methodology for evaluating ML medical imaging systems under resource-constrained conditions.

Idea and Motivation

Ukrainian healthcare faces a structural challenge: demand for accurate, fast, and accessible medical imaging diagnostics consistently outpaces the supply of trained specialists, particularly in regions outside major urban centres. Radiological capacity is unevenly distributed; waiting times for specialist review remain high; and diagnostic error rates in under-resourced settings are a documented concern.

This series began with a straightforward premise: machine learning approaches to medical image analysis are mature enough to provide meaningful decision-support in constrained clinical environments. The question is not whether ML can match specialist accuracy in controlled settings; the literature is clear that it can. The research question is whether these methods can be responsibly adapted to the realities of Ukrainian healthcare infrastructure: legacy imaging equipment, limited labelled datasets, and a regulatory framework not yet designed for algorithmic assistance.

Goal

The series set out to build a complete, reproducible evidence base for ML-assisted medical imaging diagnosis in Ukrainian healthcare. This means not only surveying and benchmarking ML architectures but constructing the analytical infrastructure around them: data governance frameworks, model evaluation standards, integration protocols for existing PACS systems, explainability requirements, and training curricula for healthcare professionals who will operate alongside these systems.

The goal is a self-contained research corpus that a healthcare institution, regulatory body, or research group could use as a foundation — not a collection of isolated experiments, but a structured methodology from first principles to clinical-readiness criteria.

Scope

The series covers 43 articles across six thematic phases:

Table 1. Research phases and thematic coverage
PhaseFocus AreaKey Topics
1FoundationsML taxonomy for medical imaging, survey of diagnostic applications, Ukrainian healthcare system context, regulatory environment, dataset availability
2Architecture ReviewConvolutional neural networks, Vision Transformers, hybrid models, transfer learning strategies, model selection for resource-constrained deployment
3ExplainabilityXAI methods (Grad-CAM, SHAP, LIME) applied to medical imaging classifiers, clinician-facing explanation design, trust calibration
4Data InfrastructureMedical imaging dataset standards, annotation protocols, privacy-preserving data sharing, synthetic data augmentation
5IntegrationScanLab tool architecture, PACS/DICOM integration specifications, clinical protocol templates, deployment patterns
6FuturesResearch gaps, curriculum design for medical AI, open research agenda for Ukrainian healthcare AI

Focus

The primary technical focus is on deep learning architectures for image classification and anomaly detection: convolutional neural networks (CNNs) including ResNet, EfficientNet, and DenseNet variants; Vision Transformers (ViTs) and hybrid CNN-ViT architectures; and transfer learning strategies that allow high-accuracy performance with limited domain-specific training data.

Explainable AI (XAI) is treated as a first-class requirement rather than an afterthought. Every architecture reviewed is evaluated against interpretability criteria relevant to clinical use. The Ukrainian healthcare context is maintained throughout — deployment assumptions reflect actual infrastructure, not idealised conditions.

Limitations

Geographic scopeAnalysis calibrated to Ukrainian healthcare infrastructure and regulatory context. Conclusions do not automatically transfer to other systems.
Data accessPublicly available benchmark datasets used. Ukrainian-specific datasets are proposed frameworks, not collected corpora.
No clinical trialsTheoretical and architectural research only. No patient data used; no prospective clinical studies conducted.
Regulatory gapIntegration specifications represent research-grade design, not certified medical device documentation.

Scientific Value

The series makes three contributions to the field. First, it provides a structured methodology for evaluating ML medical imaging systems under resource-constrained conditions — a gap in the existing literature, which is dominated by high-resource settings. Second, it documents the Ukrainian healthcare AI landscape as a research object: regulatory gaps, infrastructure constraints, and institutional readiness factors that affect deployment. Third, it advances the application of XAI in clinical settings by developing explainability evaluation criteria tied to clinical workflow requirements rather than abstract interpretability metrics.

The ScanLab tool represents a direct research artefact: an open implementation of the series’ architectural recommendations, available for replication and extension by other research groups.

Resources

Status

Complete. 43 articles published. Last updated: March 2026. No further articles are planned for this series. The research corpus is archived on Zenodo and the ScanLab tool is available for public use.

Contribution Opportunities

Researchers wishing to build on this work are encouraged to engage in the following directions:

  • Empirical validation: Conduct prospective studies using the clinical protocol templates from Phase 5 with real patient cohorts under appropriate IRB oversight.
  • Dataset construction: Build the Ukrainian medical imaging datasets proposed in Phase 4, following the annotation protocols and privacy frameworks described.
  • ScanLab extension: Add new modalities (ultrasound, MRI) or diagnostic targets to the ScanLab architecture. The GitHub repository accepts contributions.
  • Regulatory pathway: Engage with Ukrainian health regulators to develop formal approval pathways for ML-assisted diagnostic tools, using the series’ frameworks as a starting point.
  • Cross-system comparison: Adapt the methodology to other post-Soviet healthcare systems to test generalisability of the Ukrainian findings.

Published Articles

Medical Research · 33 published
Research for academic purposes only. Not a substitute for medical advice or clinical diagnosis.
All Articles
1
Image Classification and ML in Disease Recognition: A Research Review  DOI  3/10 71stabilfr·wuedophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (63 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 8, 2026 · 10 min read
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ML for Medical Diagnosis: Research Goals and Framework for Ukrainian Healthcare (Draft — in preparation)
2
Ukrainian Healthcare System: Current Medical Imaging Practices  DOI  3/10 49stabilfr·wuedophcgmx
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[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI25%○≥80% have a Digital Object Identifier
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[o]ORCID✓✓Author ORCID verified for academic identity
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[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (27 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 8, 2026 · 13 min read
3
ML Model Taxonomy for Medical Imaging  DOI  4/10 63stabilfr·wuedophcgmx
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[t]Trusted80%✓≥80% from verified, high-quality sources
[a]DOI20%○≥80% have a Digital Object Identifier
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[f]Free Access100%✓≥80% are freely accessible
[r]References10 refs✓Minimum 10 references required
[w]Words [REQ]1,041✓Minimum 1,000 words of article body. Current: 1,041
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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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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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (50 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 8, 2026 · 5 min read
4
Data Requirements and Quality Standards for Medical ML  DOI  10/10 48stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted25%○≥80% from verified, high-quality sources
[a]DOI25%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed25%○≥80% have metadata indexed
[l]Academic25%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References4 refs○Minimum 10 references required
[w]Words [REQ]2,400✓Minimum 1,000 words of article body. Current: 2,400
[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.18752898
[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%
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[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 (25 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 8, 2026 · 11 min read
5
Data Requirements and Quality Standards for Medical Imaging AI  DOI  8/10 57stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources25%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI8%○≥80% have a Digital Object Identifier
[b]CrossRef8%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access67%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]2,279✓Minimum 1,000 words of article body. Current: 2,279
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[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%
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[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (40 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 8, 2026 · 11 min read
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Regulatory Landscape for Medical AI: FDA, CE Marking, and Ukrainian MHSU (Draft — in preparation)
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US Experience: FDA-Approved AI Devices – 1,200+ Authorizations, Critical Evidence Gaps (Draft — in preparation)
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[Medical ML] Regulatory Landscape for Medical AI: FDA, CE Marking, and Ukrainian MHSU (Draft — in preparation)
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[Medical ML] US Experience: FDA-Approved AI Devices (Draft — in preparation)
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[Medical ML] EU Experience: CE-Marked Diagnostic AI (Draft — in preparation)
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[Medical ML] UK NHS AI Lab: Lessons Learned from a £250 Million National AI Programme (Draft — in preparation)
6
[Medical ML] China's Massive Medical AI Deployment  DOI  4/10 68stabilfr·wuedophcgmx
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[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]CrossRef17%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic67%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References6 refs○Minimum 10 references required
[w]Words [REQ]2,835✓Minimum 1,000 words of article body. Current: 2,835
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[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%
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[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 (59 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 8, 2026 · 14 min read
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[Medical ML] Failed Implementations: What Went Wrong (Draft — in preparation)
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[Medical ML] Physician Resistance: Causes and Solutions (Draft — in preparation)
7
[Medical ML] CNN Architectures for Medical Imaging: From ResNet to EfficientNet  DOI  7/10 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
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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]1,524✓Minimum 1,000 words of article body. Current: 1,524
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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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[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 (32 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 8, 2026 · 8 min read
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[Medical ML] Vision Transformers in Radiology: From Image Patches to Clinical Decisions (Draft — in preparation)
8
[Medical ML] Hybrid Models: Best of Both Worlds  DOI  3/10 73stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources11%○≥80% from editorially reviewed sources
[t]Trusted78%○≥80% from verified, high-quality sources
[a]DOI78%○≥80% have a Digital Object Identifier
[b]CrossRef11%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic78%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References9 refs○Minimum 10 references required
[w]Words [REQ]1,774✓Minimum 1,000 words of article body. Current: 1,774
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[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%
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[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 (67 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 8, 2026 · 9 min read
9
[Medical ML] EU Experience: CE-Marked Diagnostic AI  DOI  4/10 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
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[w]Words [REQ]2,711✓Minimum 1,000 words of article body. Current: 2,711
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[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%)
Medical Research · Feb 9, 2026 · 14 min read
10
[Medical ML] UK NHS AI Lab: Lessons Learned from £250M Programme  DOI  5/10 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]3,265✓Minimum 1,000 words of article body. Current: 3,265
[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.18752862
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
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[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%)
Medical Research · Feb 9, 2026 · 17 min read
11
[Medical ML] China's Massive Medical AI Deployment  DOI  5/10 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]3,357✓Minimum 1,000 words of article body. Current: 3,357
[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%)
Medical Research · Feb 9, 2026 · 17 min read
12
[Medical ML] Failed Implementations: What Went Wrong  DOI  5/10 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]3,497✓Minimum 1,000 words of article body. Current: 3,497
[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%)
Medical Research · Feb 9, 2026 · 18 min read
13
[Medical ML] Physician Resistance: Causes and Solutions  DOI  5/10 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]3,447✓Minimum 1,000 words of article body. Current: 3,447
[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%)
Medical Research · Feb 9, 2026 · 17 min read
14
[Medical ML] Vision Transformers in Radiology: Architecture, Applications, and Clinical Performance  DOI  4/10 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]4,238✓Minimum 1,000 words of article body. Current: 4,238
[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%)
Medical Research · Feb 9, 2026 · 21 min read
15
[Medical ML] Hybrid Models: Best of Both Worlds — CNN-Transformer Architectures for Clinical Imaging  DOI  7/10 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]3,682✓Minimum 1,000 words of article body. Current: 3,682
[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%)
Medical Research · Feb 9, 2026 · 18 min read
16
[Medical ML] Explainable AI (XAI) for Clinical Trust: Bridging the Black Box Gap  DOI  5/10 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]4,879✓Minimum 1,000 words of article body. Current: 4,879
[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%)
Medical Research · Feb 9, 2026 · 24 min read
17
[Medical ML] Transfer Learning and Domain Adaptation: Bridging the Data Gap in Medical Imaging AI  DOI  6/10 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]3,219✓Minimum 1,000 words of article body. Current: 3,219
[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.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
[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 (72 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 9, 2026 · 17 min read
18
[Medical ML] Federated Learning for Privacy-Preserving Medical AI Training: Multi-Institutional Collaboration Without Data Sharing  DOI  9/10 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]2,932✓Minimum 1,000 words of article body. Current: 2,932
[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
[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 (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 9, 2026 · 15 min read
19
[Medical ML] PACS Integration Strategies for AI-Powered Medical Imaging: A Comprehensive Framework  DOI  5/10 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]3,492✓Minimum 1,000 words of article body. Current: 3,492
[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
[h]Freshness16%○≥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 (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 9, 2026 · 17 min read
20
Medical ML: Radiologist-AI Collaboration Protocols - Designing Human-Machine Partnerships for Clinical Excellence  DOI  3/10 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]3,772✓Minimum 1,000 words of article body. Current: 3,772
[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.18704558
[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]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 9, 2026 · 19 min read
21
Medical ML: Confidence Thresholds and Escalation Protocols in Clinical AI Deployment  DOI  7/10 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]3,886✓Minimum 1,000 words of article body. Current: 3,886
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]19✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 19
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752845
[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]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 9, 2026 · 20 min read
22
Medical ML: Quality Assurance and Monitoring for Medical AI Systems  DOI  5/10 70stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources50%○≥80% from editorially reviewed sources
[t]Trusted81%✓≥80% from verified, high-quality sources
[a]DOI63%○≥80% have a Digital Object Identifier
[b]CrossRef56%○≥80% indexed in CrossRef
[i]Indexed13%○≥80% have metadata indexed
[l]Academic63%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]3,512✓Minimum 1,000 words of article body. Current: 3,512
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]13✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 13
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18709914
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness38%○≥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
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (62 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 10, 2026 · 17 min read
23
Medical ML: Training Programs for Physicians — Building AI Competency in Medical Imaging  DOI  3/10 42stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted40%○≥80% from verified, high-quality sources
[a]DOI0%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic0%○≥80% from journals/conferences/preprints
[f]Free Access80%✓≥80% are freely accessible
[r]References5 refs○Minimum 10 references required
[w]Words [REQ]4,141✓Minimum 1,000 words of article body. Current: 4,141
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]20✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 20
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752838
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness0%○≥60% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (15 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 10, 2026 · 21 min read
24
Medical ML: Ukrainian Medical Imaging Infrastructure — Current State and AI Readiness Assessment  DOI  4/10 52stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]2,387✓Minimum 1,000 words of article body. Current: 2,387
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]8✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 8
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752836
[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]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 10, 2026 · 12 min read
25
Medical ML: Language Localization for Ukrainian Medical AI User Interfaces  DOI  3/10 54stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources14%○≥80% from editorially reviewed sources
[t]Trusted52%○≥80% from verified, high-quality sources
[a]DOI29%○≥80% have a Digital Object Identifier
[b]CrossRef14%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic29%○≥80% from journals/conferences/preprints
[f]Free Access33%○≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]4,970✓Minimum 1,000 words of article body. Current: 4,970
[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.18704562
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness9%○≥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]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (35 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 10, 2026 · 25 min read
26
Medical ML: Legal Framework for AI in Ukrainian Healthcare — Regulations, Liability, and EU Harmonization  DOI  3/10 63stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources26%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI37%○≥80% have a Digital Object Identifier
[b]CrossRef26%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic37%○≥80% from journals/conferences/preprints
[f]Free Access79%○≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]4,520✓Minimum 1,000 words of article body. Current: 4,520
[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.18752832
[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]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (50 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 10, 2026 · 23 min read
27
Medical ML: Cost-Benefit Analysis of AI Implementation for Ukrainian Hospitals  DOI  5/10 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]3,737✓Minimum 1,000 words of article body. Current: 3,737
[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%)
Medical Research · Feb 10, 2026 · 18 min read
28
Medical ML: Comprehensive Framework for ML-Based Medical Imaging Diagnosis — Ukrainian Implementation Guide  DOI  3/10 62stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources36%○≥80% from editorially reviewed sources
[t]Trusted68%○≥80% from verified, high-quality sources
[a]DOI45%○≥80% have a Digital Object Identifier
[b]CrossRef32%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access32%○≥80% are freely accessible
[r]References22 refs✓Minimum 10 references required
[w]Words [REQ]3,330✓Minimum 1,000 words of article body. Current: 3,330
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]25✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 25
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730557
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness17%○≥60% of references from 2025–2026. Current: 17%
[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 (49 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 11, 2026 · 17 min read
29
Medical ML: ScanLab Integration Specifications — Technical Architecture for Ukrainian Healthcare AI  DOI  4/10 42stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted15%○≥80% from verified, high-quality sources
[a]DOI10%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic10%○≥80% from journals/conferences/preprints
[f]Free Access25%○≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]3,973✓Minimum 1,000 words of article body. Current: 3,973
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]25✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 25
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730555
[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]Diagrams10✓Mermaid architecture/flow diagrams. Current: 10
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (15 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 11, 2026 · 22 min read
30
Medical ML: Clinical Protocol Templates for ML-Assisted Medical Imaging Diagnosis  DOI  3/10 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]4,028✓Minimum 1,000 words of article body. Current: 4,028
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]25✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 25
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730553
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness0%○≥60% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 11, 2026 · 21 min read
31
Medical ML: Training Curriculum for Medical AI — Healthcare Professional Development Framework  DOI  3/10 47stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted40%○≥80% from verified, high-quality sources
[a]DOI20%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic20%○≥80% from journals/conferences/preprints
[f]Free Access80%✓≥80% are freely accessible
[r]References5 refs○Minimum 10 references required
[w]Words [REQ]3,256✓Minimum 1,000 words of article body. Current: 3,256
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]25✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 25
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665639
[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]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (23 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 11, 2026 · 1 min read
32
Medical ML: Open Questions for Future Research — A Medical AI Research Agenda for Ukrainian Healthcare  DOI  2/10 49stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI25%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic25%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References4 refs○Minimum 10 references required
[w]Words [REQ]3,350✓Minimum 1,000 words of article body. Current: 3,350
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]25✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 25
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665637
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness0%○≥60% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (27 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Feb 11, 2026 · 17 min read
33
Tattoo-Based Emergency Patient Identification: From Internal Research to Public Deployment  DOI  4/10 70stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources25%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI58%○≥80% have a Digital Object Identifier
[b]CrossRef25%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic58%○≥80% from journals/conferences/preprints
[f]Free Access75%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]1,986✓Minimum 1,000 words of article body. Current: 1,986
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]8✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 8
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18929669
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness10%○≥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 (62 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical Research · Mar 9, 2026 · 10 min read
33 published18,481 total views515 min total readingFeb 2026 – Mar 2026 published