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US Experience: FDA-Approved AI Devices – 1,200+ Authorizations, Critical Evidence Gaps

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752892  75stabilfr·wdophcgmx
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Score = Ref Trust (91 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

As Ukraine develops its regulatory framework for medical AI (aligned with EU MDR through recent reforms), understanding the world's largest medical AI market provides invaluable lessons. The US FDA has authorized over 1,200 AI/ML-enabled medical devices—more than any other regulatory body—making it the de facto testing ground for medical AI deployment.

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752892 75stabilfr·wdophcgmx
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Score = Ref Trust (91 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Regulatory Landscape for Medical AI: FDA, CE Marking, and Ukrainian MHSU

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752894  81stabilfr·wdophcgmx
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Score = Ref Trust (91 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

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 81stabilfr·wdophcgmx
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Score = Ref Trust (91 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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Data Requirements and Quality Standards for Medical Imaging AI

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752896  49stabilfr·wdophcgmx
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Score = Ref Trust (48 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

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 49stabilfr·wdophcgmx
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Score = Ref Trust (48 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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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  81stabilfr·wdophcgmx
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Score = Ref Trust (91 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

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 81stabilfr·wdophcgmx
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Score = Ref Trust (91 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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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
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752908  49stabilfr·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 49stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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Score = Ref Trust (57 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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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
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752910  66stabilfr·wdophcgmx
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[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI80%✓≥80% have a Digital Object Identifier
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752910
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]0%✗≥60% of references from 2025–2026. Current: 0%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (86 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

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 66stabilfr·wdophcgmx
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[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI80%✓≥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]0%✗≥60% of references from 2025–2026. Current: 0%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (86 × 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  23stabilfr·wdophcgmx
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[t]Trusted17%○≥80% from verified, high-quality sources
[a]DOI17%○≥80% have a Digital Object Identifier
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[l]Academic17%○≥80% from journals/conferences/preprints
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[r]References6 refs○Minimum 10 references required
[w]Words [REQ]891✗Minimum 2,000 words for a full research article. Current: 891
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752914
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (18 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)

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 23stabilfr·wdophcgmx
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[t]Trusted17%○≥80% from verified, high-quality sources
[a]DOI17%○≥80% have a Digital Object Identifier
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[w]Words [REQ]891✗Minimum 2,000 words for a full research article. Current: 891
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752914
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]22%✗≥60% of references from 2025–2026. Current: 22%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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[m]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (18 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)
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2025 AI Research Impact: A Year of Transformation

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DOI: 10.5281/zenodo.18752916  39stabilfr·wdophcgmx
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]Indexed50%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access75%○≥80% are freely accessible
[r]References4 refs○Minimum 10 references required
[w]Words [REQ]1,624✗Minimum 2,000 words for a full research article. Current: 1,624
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752916
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥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 (41 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

2025 marked a fundamental shift in artificial intelligence research—transitioning from "powerful tool" to "fundamental infrastructure." This comprehensive review examines the year's transformative achievements across model efficiency, reasoning capabilities, multimodal intelligence, and real-world deployment. We analyze key breakthroughs including the evolution of the Gemini model series, the e...

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DOI: 10.5281/zenodo.18752916 39stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI25%○≥80% have a Digital Object Identifier
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[i]Indexed50%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access75%○≥80% are freely accessible
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[w]Words [REQ]1,624✗Minimum 2,000 words for a full research article. Current: 1,624
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752916
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥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 (41 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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