š Academic Citation: Ivchenko, O. (2026). [Medical ML] Failed Implementations: What Went Wrong. Medical Machine Learning for Diagnosis Series. Odesa National Polytechnic University. DOI: 10.5281/zenodo.18752858 Abstract The healthcare artificial intelligence literature predominantly features success stories, creating a survivorship bias that inadequately prepares implementers for the challenges of real-world deployment. This paper addresses this gap through…
Category: Medical ML Diagnosis
ML for Medical Imaging Diagnosis
[Medical ML] China’s Massive Medical AI Deployment
š Medical Machine Learning Research Series Chinas massive medical AI deployment and adoption China’s Massive Medical AI Deployment: Scale, Strategy, and Implications for Global Healthcare Transformation š¤ Oleh Ivchenko, PhD Candidate šļø Medical AI Research Laboratory, Odessa National Polytechnic University (ONPU) š February 2026 China Healthcare AI Large-Scale Deployment Digital Health Infrastructure NMPA Regulation Healthcare…
[Medical ML] UK NHS AI Lab: Lessons Learned from Ā£250M Programme
š Medical Machine Learning Research Series Lessons learned from UK NHS 250 million AI programme UK NHS AI Lab: Lessons Learned from the Ā£250M Programme ā Infrastructure, Implementation, and Impact Assessment š¤ Oleh Ivchenko, PhD Candidate šļø Medical AI Research Laboratory, Odessa National Polytechnic University (ONPU) š February 2026 NHS AI Lab United Kingdom Healthcare…
[Medical ML] EU Experience: CE-Marked Diagnostic AI
š Academic Citation: Ivchenko, O. (2026). EU Experience: CE-Marked Diagnostic AI ā A Comprehensive Analysis of Regulatory Frameworks and Clinical Implementation. Medical ML Diagnosis Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18695004 Abstract The European Union has emerged as a global leader in establishing comprehensive regulatory frameworks for artificial intelligence in medical diagnostics, with the CE…
[Medical ML] Hybrid Models: Best of Both Worlds
š 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…
[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
š 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…
[Medical ML] Failed Implementations: What Went Wrong
Article #11 in Medical ML for Ukrainian Doctors Series Understanding failed medical AI implementations By Oleh Ivchenko | Researcher, ONPU | Stabilarity Hub | February 8, 2026 š 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
š 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….