📚 Medical Machine Learning Research Series Failed Implementations: What Went Wrong — Systematic Analysis of Healthcare AI Project Failures and Lessons for Future Deployments 👤 Oleh Ivchenko, PhD Candidate 🏛️ Medical AI Research Laboratory, Taras Shevchenko National University of Kyiv 📅 February 2026 Implementation Failure Lessons Learned Healthcare AI Case Studies Risk Mitigation 📋 Abstract…
Category: Medical ML Diagnosis
ML for Medical Imaging Diagnosis
[Medical ML] China’s Massive Medical AI Deployment
📚 Medical Machine Learning Research Series China’s Massive Medical AI Deployment: Scale, Strategy, and Implications for Global Healthcare Transformation 👤 Oleh Ivchenko, PhD Candidate 🏛️ Medical AI Research Laboratory, Taras Shevchenko National University of Kyiv 📅 February 2026 China Healthcare AI Large-Scale Deployment Digital Health Infrastructure NMPA Regulation Healthcare Access 📋 Abstract China has emerged…
[Medical ML] UK NHS AI Lab: Lessons Learned from £250M Programme
📚 Medical Machine Learning Research Series UK NHS AI Lab: Lessons Learned from the £250M Programme — Infrastructure, Implementation, and Impact Assessment 👤 Oleh Ivchenko, PhD Candidate 🏛️ Medical AI Research Laboratory, Taras Shevchenko National University of Kyiv 📅 February 2026 NHS AI Lab United Kingdom Healthcare Infrastructure Digital Health Public Healthcare AI 📋 Abstract…
[Medical ML] EU Experience: CE-Marked Diagnostic AI
📚 Medical Machine Learning Research Series EU Experience: CE-Marked Diagnostic AI — A Comprehensive Analysis of Regulatory Frameworks and Clinical Implementation 👤 Oleh Ivchenko, PhD Candidate 🏛️ Medical AI Research Laboratory, Taras Shevchenko National University of Kyiv 📅 February 2026 CE Marking Medical Device Regulation AI Diagnostics European Union Healthcare AI 📋 Abstract The European…
[Medical ML] Hybrid Models: Best of Both Worlds
# Hybrid Models: Best of Both Worlds **Author:** Oleh Ivchenko **Published:** February 8, 2026 **Series:** ML for Medical Diagnosis Research **Article:** 15 of 35 — ## Executive Summary 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…
[Medical ML] Vision Transformers in Radiology: From Image Patches to Clinical Decisions
# 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…
[Medical ML] CNN Architectures for Medical Imaging: From ResNet to EfficientNet
# 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…
[Medical ML] Physician Resistance: Causes and Solutions
Physician Resistance: Causes and Solutions Article #12 in Medical ML for Ukrainian Doctors Series By Oleh Ivchenko | Researcher, ONPU | Stabilarity Hub | February 8, 2026 📋 Key Questions Addressed What psychological, professional, and structural factors drive physician resistance to medical AI? How does familiarity with AI influence physician attitudes? What evidence-based approaches successfully…
[Medical ML] Failed Implementations: What Went Wrong
Failed Implementations: What Went Wrong Article #11 in Medical ML for Ukrainian Doctors Series 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…
[Medical ML] China’s Massive Medical AI Deployment
China’s Massive Medical AI Deployment Article #10 in Medical ML for Ukrainian Doctors Series By Oleh Ivchenko | Researcher, ONPU | Stabilarity Hub | February 8, 2026 📋 Key Questions Addressed How has China scaled medical AI deployment to become the world’s fastest-growing healthcare AI market? What regulatory, infrastructural, and policy factors enabled China’s rapid…