📚 Academic Citation: Ivchenko, O. (2026). Quality Assurance and Monitoring for Medical AI Systems. Medical ML Diagnosis Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18709914 Abstract The deployment of machine learning algorithms in clinical diagnostics represents one of healthcare’s most significant technological advances. However, unlike traditional medical devices, AI systems are uniquely susceptible to performance degradation…
Category: Medical ML Diagnosis
ML for Medical Imaging Diagnosis
Medical ML: Confidence Thresholds and Escalation Protocols in Clinical AI Deployment
📚 Academic Citation: Ivchenko, O. (2026). Confidence Thresholds and Escalation Protocols in Clinical AI Deployment. Medical ML Research Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18752845 Author: Oleh Ivchenko, PhD Candidate Affiliation: Odessa National Polytechnic University (ONPU) | Stabilarity Hub Research Series: Machine Learning for Medical Diagnosis — Article 21 of 35 Date: February 9, 2026…
Medical ML: Radiologist-AI Collaboration Protocols – Designing Human-Machine Partnerships for Clinical Excellence
📚 Academic Citation: Ivchenko, O.. (2026). Medical ML: Radiologist-AI Collaboration Protocols – Designing Human-Machine Partnerships for Clinical Excellence. Medical ML Diagnosis Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18704558 Abstract The integration of artificial intelligence into radiology practice represents more than a technological upgrade—it constitutes a fundamental reimagining of diagnostic workflows that have remained largely unchanged…
[Medical ML] PACS Integration Strategies for AI-Powered Medical Imaging: A Comprehensive Framework
# PACS Integration Strategies for AI-Powered Medical Imaging: A Comprehensive Framework for Clinical Deployment **Author:** Oleh Ivchenko, PhD Candidate **Affiliation:** Odessa National Polytechnic University (ONPU) | Stabilarity Hub **Series:** Medical ML for Diagnosis — Article 19 of 35 **Date:** February 9, 2026 **Category:** Clinical Workflow Integration — ## Abstract The integration of artificial intelligence (AI)…
[Medical ML] Federated Learning for Privacy-Preserving Medical AI Training: Multi-Institutional Collaboration Without Data Sharing
📚 Academic Citation: Ivchenko, O. (2026). Federated Learning for Privacy-Preserving Medical AI Training: Multi-Institutional Collaboration Without Data Sharing. Medical ML for Diagnosis Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18685263 Abstract Federated learning (FL) represents a paradigm shift in collaborative machine learning that enables multiple healthcare institutions to jointly train diagnostic AI models without sharing sensitive…
[Medical ML] Transfer Learning and Domain Adaptation: Bridging the Data Gap in Medical Imaging AI
📚 Academic Citation: Ivchenko, O. (2026). Transfer Learning and Domain Adaptation: Bridging the Data Gap in Medical Imaging AI. Medical ML Diagnosis Series. Odesa National Polytechnic University. DOI: 10.5281/zenodo.18672185 Abstract The remarkable success of deep learning in medical imaging has been tempered by a fundamental challenge: the scarcity of large-scale, annotated medical datasets essential for…
[Medical ML] Explainable AI (XAI) for Clinical Trust: Bridging the Black Box Gap
📚 Academic Citation: Ivchenko, O. (2026). Explainable AI (XAI) for Clinical Trust: Bridging the Black Box Gap in Medical Imaging Diagnostics. Medical ML Research Series. Odesa National Polytechnic University. Abstract The deployment of deep learning models in clinical radiology has achieved remarkable diagnostic accuracy, often matching or exceeding human expert performance. However, these models remain…
[Medical ML] Hybrid Models: Best of Both Worlds — CNN-Transformer Architectures for Clinical Imaging
📚 Academic Citation: Ivchenko, O. (2026). [Medical ML] Hybrid Models: Best of Both Worlds — CNN-Transformer Architectures for Clinical Imaging. Medical Machine Learning for Diagnosis Series. Odesa National Polytechnic University. DOI: 10.5281/zenodo.18752852 Abstract The convergence of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) represents a paradigm shift in medical image analysis, addressing the fundamental…
[Medical ML] Vision Transformers in Radiology: Architecture, Applications, and Clinical Performance
📚 Academic Citation: Oleh Ivchenko. (2026). Vision Transformers in Radiology: Architecture, Applications, and Clinical Performance. Medical ML Diagnosis Series, Article 14. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18672181 Abstract Vision Transformers (ViT) represent a paradigm shift in medical image analysis, applying the revolutionary attention mechanism from natural language processing to radiological imaging. This comprehensive review examines…
[Medical ML] Physician Resistance: Causes and Solutions
📚 Academic Citation: Ivchenko, O. (2026). Physician Resistance to Healthcare AI: Understanding Causes and Building Collaborative Practice. Medical ML Research Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18752854 👤 Oleh Ivchenko, PhD Candidate 🏛️ Medical AI Research Laboratory, Odessa National Polytechnic University (ONPU) 📅 February 2026 Physician Adoption Technology Acceptance Healthcare AI Implementation Change Management Human-AI…