What question did this study set out to answer?

This paper aims to develop a framework for deploying machine learning in medical imaging within Ukrainian healthcare systems.

February 13, 2026Open Access

Machine Learning for Medical Imaging Diagnosis: A Comprehensive Framework for Ukrainian Healthcare Implementation

Puntos clave

This paper aims to develop a framework for deploying machine learning in medical imaging within Ukrainian healthcare systems.
Synthesis of 22 studies on global best practices in machine learning and medical imaging.
Evaluation of technical architectures, including CNN and transformer models.
Assessment of Ukrainian-specific adaptations for effective clinical integration.
Proposed framework facilitates collaboration between radiologists and AI systems.
Identified necessary regulatory compliance and infrastructure adaptations for Ukraine.
Successfully integrated explainable AI components for improved trust and usability.

Resumen

This comprehensive paper synthesizes research on implementing machine learning systems for medical imaging diagnosis in Ukrainian healthcare settings. Drawing from 22 detailed studies covering global best practices, technical architectures, clinical integration protocols, and Ukrainian-specific adaptations, we present a complete framework for deploying AI-powered diagnostic systems. The framework addresses CNN and transformer architectures, explainable AI, federated learning, PACS integration, radiologist collaboration protocols, Ukrainian infrastructure assessment, language localization, regulatory compliance, and the ScanLab reference implementation.Authors: Ivchenko, Oleg (IT company Lohika, Capgemini Engineering, Odessa National Polytechnic University, Department of Economic Cybernetics and Information Technologies) Researcher Dmytro Grybeniuk, Dmytro Grybeniuk (Irvine Valley College, Odessa National Polytechnic University) Research group Ivchenko, Iryna (Odessa National Polytechnic University) Research group

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Cite This Study

Ивченко et al. (Wed,) studied this question.

synapsesocial.com/papers/698ebf4385a1ff6a930167d3 https://doi.org/https://doi.org/10.5281/zenodo.18615148

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