Author: Yoman

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

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752862  70stabilfr·wdophcgmx
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The United Kingdom's National Health Service AI Lab, established in 2019 with a £250 million investment, represents one of the most ambitious national initiatives to accelerate artificial intelligence adoption in public healthcare. This comprehensive analysis examines the programme's evolution, achievements, challenges, and transferable lessons over its five-year operational history. Through sy...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752862 70stabilfr·wdophcgmx
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[Medical ML] EU Experience: CE-Marked Diagnostic AI

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18695004  72stabilfr·wdophcgmx
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The European Union has emerged as a global leader in establishing comprehensive regulatory frameworks for artificial intelligence in medical diagnostics, with the CE marking process serving as the cornerstone of quality assurance and patient safety. This paper presents an extensive analysis of the EU's experience with CE-marked diagnostic AI systems, examining the regulatory journey from the Me...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18695004 72stabilfr·wdophcgmx
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[Medical ML] Hybrid Models: Best of Both Worlds

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752864  65stabilfr·wdophcgmx
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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...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752864 65stabilfr·wdophcgmx
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[Medical ML] Vision Transformers in Radiology: From Image Patches to Clinical Decisions

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752868  75stabilfr·wdophcgmx
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Vision Transformers (ViTs) have emerged as a transformative architecture in medical imaging, challenging the decade-long dominance of Convolutional Neural Networks (CNNs). Unlike CNNs that build understanding through hierarchical local feature extraction, ViTs treat images as sequences of patches and leverage self-attention mechanisms to capture global context from the first layer. This compreh...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752868 75stabilfr·wdophcgmx
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[Medical ML] CNN Architectures for Medical Imaging: From ResNet to EfficientNet

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752870  75stabilfr·wdophcgmx
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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...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752870 75stabilfr·wdophcgmx
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[Medical ML] Physician Resistance: Causes and Solutions

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752874  69stabilfr·wdophcgmx
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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...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752874 69stabilfr·wdophcgmx
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[Medical ML] Failed Implementations: What Went Wrong

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752878  

Despite over $66.8 billion invested globally in healthcare AI (2021 alone), the field has produced spectacular failures alongside its successes. Understanding what went wrong—and why—is essential for any hospital considering AI adoption.

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752878
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[Medical ML] China’s Massive Medical AI Deployment

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18645077  72stabilfr·wdophcgmx
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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...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18645077 72stabilfr·wdophcgmx
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[Medical ML] UK NHS AI Lab: Lessons Learned from a £250 Million National AI Programme

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18672171  81stabilfr·wdophcgmx
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Score = Ref Trust (91 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

The UK's NHS AI Lab, operating from 2019 to 2025 with £250 million in initial funding, represents the world's most ambitious national attempt to systematically deploy artificial intelligence in healthcare. This analysis examines the programme's comprehensive evaluation, documenting both its remarkable achievements—including £44 million in demonstrated cost savings and the development of crucial...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18672171 81stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
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[g]Code—○Source code available on GitHub
[m]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
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Score = Ref Trust (91 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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