Author: Yoman

📚 Academic Citation: Ivchenko, O. (2026). Hybrid Models: Best of Both Worlds. ML for Medical Diagnosis Research Series, Article 15. Odesa National Polytechnic University. DOI: 10.5281/zenodo.14828792 Abstract 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…

# Vision Transformers in Radiology: From Image Patches to Clinical Decisions **Author:** Oleh Ivchenko **Published:** February 8, 2026 **Series:** ML for Medical Diagnosis Research **Article:** 14 of 35 — ## Executive Summary Vision Transformers (ViTs) have emerged as a transformative architecture in medical imaging, challenging the decade-long dominance of Convolutional Neural Networks (CNNs). Unlike CNNs…

# CNN Architectures for Medical Imaging: From ResNet to EfficientNet *By Oleh Ivchenko | February 8, 2026* Convolutional Neural Networks (CNNs) have fundamentally transformed medical image analysis, evolving from simple feature extractors to sophisticated architectures capable of matching or exceeding radiologist-level performance. This article provides a comprehensive technical deep-dive into the CNN architectures that power…

📚 Academic Citation: Ivchenko, O. (2026). Physician Resistance: Causes and Solutions. Medical ML for Ukrainian Doctors Series, Article 12. Odesa National Polytechnic University. DOI: 10.5281/zenodo.14822441 Abstract 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…

Article #11 in Medical ML for Ukrainian Doctors Series Understanding failed medical AI implementations By Oleh Ivchenko | Researcher, ONPU | Stabilarity Hub | February 8, 2026 📋 Key Questions Addressed What are the most significant high-profile failures of medical AI implementations? What technical, organizational, and deployment factors cause AI systems to fail? What lessons…

📚 Academic Citation: Ivchenko, O. (2026). China’s Massive Medical AI Deployment: Lessons for Emerging Healthcare AI Ecosystems. Medical ML Diagnosis Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18695003 Abstract 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….

📚 Academic Citation: Ivchenko, O. (2026). UK NHS AI Lab: Lessons Learned from a £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 £250 million in initial funding, represents the world’s most ambitious national attempt to systematically…