Regulatory Landscape for Medical AI: FDA, CE Marking, and Ukrainian MHSU

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752894  40stabilfr·wdophcgmx
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Navigating the regulatory landscape for medical AI requires understanding three distinct frameworks: the FDA's mature Software as Medical Device (SaMD) pathway with over 1,200 approved AI/ML devices, the EU's dual MDR/AI Act compliance burden, and Ukraine's transitional system awaiting MDR harmonization. This analysis maps pathways for ScanLab and similar Ukrainian medical AI initiatives, ident...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752894 40stabilfr·wdophcgmx
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Data Requirements and Quality Standards for Medical Imaging AI

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752896  45stabilfr·wdophcgmx
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This article examines the critical data quality standards required for medical imaging AI systems, revealing that of 1,016 FDA-approved AI medical devices, 93.3% did not report training data source and 76.3% lacked demographic information. We establish a comprehensive framework for data quality assessment including the six pillars of medical imaging data quality, bias sources and mitigation str...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752896 45stabilfr·wdophcgmx
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Data Requirements and Quality Standards for Medical ML

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752898  36stabilfr·wdophcgmx
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Models pre-trained on a Collection of Public Medical Image Datasets (CPMID) covering X-ray, CT, and MRI outperformed ImageNet pre-training by:

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752898 36stabilfr·wdophcgmx
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ML Model Taxonomy for Medical Imaging

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752900  43stabilfr·wdophcgmx
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Article #4 in "Machine L[REDACTED]g for Medical Diagnosis" Research Series By Oleh Ivchenko, Researcher, ONPU | Stabilarity Hub | February 8, 2026 Questions Addressed: How do CNN, ViT, and hybrid models compare for medical imaging? Which architecture is best for specific modalities?

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752900 43stabilfr·wdophcgmx
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Ukrainian Healthcare System: Current Medical Imaging Practices

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752902  21stabilfr·wdophcgmx
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Ukraine's healthcare system represents a unique case study in digital transformation under extraordinary circumstances. The two-level electronic healthcare system (EHS), with 36 million registered patients and 1.6 billion electronic medical records, provides a robust foundation for AI integration—despite wartime challenges that reduced viable Medical Information System (MIS) providers from 40 t...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752902 21stabilfr·wdophcgmx
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State of Medical AI Adoption: 1,200 Devices Approved, 81% of Hospitals at Zero

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DOI: 10.5281/zenodo.18752906  40stabilfr·wdophcgmx
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Global medical AI has exploded with 1,200+ FDA-approved devices, yet 81% of US hospitals have no AI adoption. Article #2 maps the adoption paradox, regional variation, success rates by use case, and the critical barriers—with lessons for Ukrainian healthcare.

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DOI: 10.5281/zenodo.18752906 40stabilfr·wdophcgmx
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6-phase research framework for ML-augmented medical diagnosis

ML for Medical Diagnosis: Research Goals and Framework for Ukrainian Healthcare

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752908  43stabilfr·wdophcgmx
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Launching a 12-week research program to build a practical framework for ML-augmented medical image diagnosis in Ukrainian healthcare. Article #1 establishes methodology, introduces Stabilarity Hub ecosystem, and outlines the path from research to ScanLab implementation.

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752908 43stabilfr·wdophcgmx
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Diagram showing how ML integrates into medical image analysis workflow from acquisition to diagnosis

Image Classification and ML in Disease Recognition: A Research Review

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752910  52stabilfr·wdophcgmx
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A comprehensive review of machine learning in medical image analysis, examining which ML techniques apply at each diagnostic stage, evidence-based best practices for doctor-AI collaboration, and unique conclusions on reducing diagnostic errors.

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752910 52stabilfr·wdophcgmx
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Chart comparing AI model training costs from GPT-4 at 00M+ to DeepSeek-R1 at /usr/bin/bash.25M

Cost-Effective AI Development: A Research Review

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DOI: 10.5281/zenodo.18752912  15stabilfr·wdophcgmx
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Score = Ref Trust (1 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

A comprehensive review of research on cost-effective AI development, examining how organizations achieve state-of-the-art capabilities at 400x lower costs through techniques like RLVR, MoE architectures, and open-weight models.

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DOI: 10.5281/zenodo.18752912 15stabilfr·wdophcgmx
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Score = Ref Trust (1 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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🚀 StabilarityHub Leads International MedAI Hackathon 2025: Transforming Healthcare with AI

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DOI: 10.5281/zenodo.18752914  22stabilfr·wdophcgmx
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Celebrating the International MedAI Hackathon 2025 — where 50+ innovators from Ukraine, Germany and beyond collaborated to build transformative AI solutions in radiology, mental health, and healthcare operations. Led by StabilarityHub with ONPU, GROMUS, Innova Clinics, and ScanLab. Discover the winning projects and the future of healthcare technology.

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DOI: 10.5281/zenodo.18752914 22stabilfr·wdophcgmx
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Score = Ref Trust (16 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)
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