Author: Yoman

[Medical ML] UK NHS AI Lab: Lessons Learned from £250M Programme

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752862  74stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources60%○≥80% from editorially reviewed sources
[t]Trusted80%✓≥80% from verified, high-quality sources
[a]DOI70%○≥80% have a Digital Object Identifier
[b]CrossRef70%○≥80% indexed in CrossRef
[i]Indexed20%○≥80% have metadata indexed
[l]Academic70%○≥80% from journals/conferences/preprints
[f]Free Access20%○≥80% are freely accessible
[r]References10 refs✓Minimum 10 references required
[w]Words [REQ]3,254✓Minimum 1,000 words of article body. Current: 3,254
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]9✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 9
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752862
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness0%○≥60% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (68 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

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

Show moreHide
Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752862 74stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources60%○≥80% from editorially reviewed sources
[t]Trusted80%✓≥80% from verified, high-quality sources
[a]DOI70%○≥80% have a Digital Object Identifier
[b]CrossRef70%○≥80% indexed in CrossRef
[i]Indexed20%○≥80% have metadata indexed
[l]Academic70%○≥80% from journals/conferences/preprints
[f]Free Access20%○≥80% are freely accessible
[r]References10 refs✓Minimum 10 references required
[w]Words [REQ]3,254✓Minimum 1,000 words of article body. Current: 3,254
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]9✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 9
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752862
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness0%○≥60% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (68 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical ML DiagnosisRead More
Read more

[Medical ML] EU Experience: CE-Marked Diagnostic AI

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18695004  75stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources45%○≥80% from editorially reviewed sources
[t]Trusted82%✓≥80% from verified, high-quality sources
[a]DOI82%✓≥80% have a Digital Object Identifier
[b]CrossRef45%○≥80% indexed in CrossRef
[i]Indexed18%○≥80% have metadata indexed
[l]Academic82%✓≥80% from journals/conferences/preprints
[f]Free Access45%○≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]2,705✓Minimum 1,000 words of article body. Current: 2,705
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]10✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 10
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18695004
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness8%○≥60% of references from 2025–2026. Current: 8%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (71 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

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

Show moreHide
Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18695004 75stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources45%○≥80% from editorially reviewed sources
[t]Trusted82%✓≥80% from verified, high-quality sources
[a]DOI82%✓≥80% have a Digital Object Identifier
[b]CrossRef45%○≥80% indexed in CrossRef
[i]Indexed18%○≥80% have metadata indexed
[l]Academic82%✓≥80% from journals/conferences/preprints
[f]Free Access45%○≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]2,705✓Minimum 1,000 words of article body. Current: 2,705
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]10✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 10
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18695004
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness8%○≥60% of references from 2025–2026. Current: 8%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (71 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical ML DiagnosisRead More
Read more

[Medical ML] Hybrid Models: Best of Both Worlds

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752864  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]1,772✓Minimum 1,000 words of article body. Current: 1,772
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]6✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 6
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752864
[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 (67 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

Hybrid architectures that combine convolutional neural networks (CNNs) with transformer-based modules are rapidly becoming the pragmatic choice for medical imaging tasks. They balance CNNs' efficiency and inductive biases with transformers' long-range context modeling. This article summarizes the state of hybrid models, evaluation results, and deployment recommendations for Ukrainian healthcare...

Show moreHide
Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752864 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]1,772✓Minimum 1,000 words of article body. Current: 1,772
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]6✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 6
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752864
[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 (67 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical ML DiagnosisRead More
Read more

[Medical ML] CNN Architectures for Medical Imaging: From ResNet to EfficientNet

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752870  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]1,517✓Minimum 1,000 words of article body. Current: 1,517
[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.18752870
[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]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%)

Academic Citation: Ivchenko, O. (2026). [Medical ML] CNN Architectures for Medical Imaging: From ResNet to EfficientNet. Medical Machine Learning for Diagnosis Series. Odesa National Polytechnic University. DOI: 10.5281/zenodo.14963752 *By Oleh Ivchenko | February 8, 2026* Convolutional Neural Networks (CNNs) have fundamentally transformed medical image analysis, evolving from simple feature ex...

Show moreHide
Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752870 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]1,517✓Minimum 1,000 words of article body. Current: 1,517
[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.18752870
[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]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 ML DiagnosisRead More
Read more

[Medical ML] Physician Resistance: Causes and Solutions

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.23486180  79stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources45%○≥80% from editorially reviewed sources
[t]Trusted82%✓≥80% from verified, high-quality sources
[a]DOI73%○≥80% have a Digital Object Identifier
[b]CrossRef45%○≥80% indexed in CrossRef
[i]Indexed55%○≥80% have metadata indexed
[l]Academic73%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]1,790✓Minimum 1,000 words of article body. Current: 1,790
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]6✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 6
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23486180
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness60%✓≥60% of references from 2025–2026. Current: 60%
[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 (74 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

The integration of artificial intelligence into clinical practice faces a critical bottleneck: physician resistance. Despite over $66 billion invested globally in healthcare AI, adoption remains stubbornly low. This article examines the multifaceted causes of physician resistance—spanning professional identity threats, liability concerns, and workflow disruption—and presents evidence-based stra...

Show moreHide
Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.23486180 79stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources45%○≥80% from editorially reviewed sources
[t]Trusted82%✓≥80% from verified, high-quality sources
[a]DOI73%○≥80% have a Digital Object Identifier
[b]CrossRef45%○≥80% indexed in CrossRef
[i]Indexed55%○≥80% have metadata indexed
[l]Academic73%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]1,790✓Minimum 1,000 words of article body. Current: 1,790
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]6✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 6
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23486180
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness60%✓≥60% of references from 2025–2026. Current: 60%
[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 (74 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)
Medical ML DiagnosisRead More
Read more

[Medical ML] China’s Massive Medical AI Deployment

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18645077  68stabilfr·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]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]2,831✓Minimum 1,000 words of article body. Current: 2,831
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]3✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 3
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18645077
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness0%○≥60% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (59 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

China has emerged as the world's fastest-growing healthcare AI market, demonstrating that large-scale medical AI deployment is achievable through coordinated policy, infrastructure investment, and strategic regulatory frameworks. This article provides comprehensive analysis of China's medical AI ecosystem, examining market growth from $900 million in 2020 to a projected $18.9 billion by 2030, t...

Show moreHide
Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18645077 68stabilfr·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]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]2,831✓Minimum 1,000 words of article body. Current: 2,831
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]3✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 3
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18645077
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness0%○≥60% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (59 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Medical ML DiagnosisRead More
Read more