ML for Medical Imaging Diagnosis
š Academic Citation: Ivchenko, O. (2026). UK NHS AI Lab: Lessons Learned from a Ā£250 Million National AI Programme. Medical ML Diagnosis Series. Odesa National Polytechnic University. DOI: 10.5281/zenodo.18672171 Abstract The UK’s NHS AI Lab, operating from 2019 to 2025 with Ā£250 million in initial funding, represents the world’s most ambitious national attempt to systematically…
EU Experience: CE-Marked Diagnostic AI Article #8 in Medical ML for Ukrainian Doctors Series EU CE-marked diagnostic AI experience and standards By Oleh Ivchenko | Researcher, ONPU | Stabilarity Hub | February 8, 2026 š Key Questions Addressed How does the European regulatory framework for medical AI differ from the US FDA approach? What is…
Article #7 in Medical ML for Ukrainian Doctors Series FDA-approved AI devices and clinical evidence By Oleh Ivchenko | Researcher, ONPU | Stabilarity Hub | February 8, 2026 š Key Questions Addressed How has the US regulatory landscape shaped AI medical device development, and what does the current FDA approval landscape look like? What evidence…
š Academic Citation: Ivchenko, O. (2026). Regulatory Landscape for Medical AI: FDA, CE Marking, and Ukrainian MHSU. Medical ML Diagnosis Series. Odesa National Polytechnic University. Article #6 in Medical ML for Ukrainian Doctors Series By Oleh Ivchenko | Researcher, ONPU | Stabilarity Hub | February 8, 2026 š Key Questions Addressed How do FDA, EU,…
US Experience: FDA-Approved AI Devices Article #7 in Medical ML for Ukrainian Doctors Series FDA-approved AI devices critical evidence analysis By Oleh Ivchenko | Researcher, ONPU | Stabilarity Hub | February 8, 2026 š Key Questions Addressed How has the US regulatory landscape shaped AI medical device development, and what does the current FDA approval…
š Academic Citation: Ivchenko, O. (2026). Regulatory Landscape for Medical AI: FDA, CE Marking, and Ukrainian MHSU. Medical ML Diagnosis Series. Odesa National Polytechnic University. DOI: 10.5281/zenodo.14672187 Abstract 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,…
š Academic Citation: Ivchenko, O. (2026). Data Requirements and Quality Standards for Medical Imaging AI. Machine Learning for Medical Diagnosis Research Series. ONPU / Stabilarity Research Hub. Abstract 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…
š Academic Citation: Ivchenko, O. (2026). Data Requirements and Quality Standards for Medical ML. Medical ML Diagnosis Series. Odesa National Polytechnic University. DOI: Pending Zenodo registration 1. The Data Quality Framework Medical imaging datasets require four fundamental qualities: Quality Dimension Definition Measurement Volume Number of samples per class 1K-100K+ depending on task Annotation Label accuracy…
š§ ML Model Taxonomy for Medical Imaging Article #4 in “Machine Learning 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? ML model taxonomy for medical imaging…
š Academic Citation: Ivchenko, O. (2026). Ukrainian Healthcare System: Current Medical Imaging Practices. Medical ML Research Series. Odesa National Polytechnic University. Abstract 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…