Medical ML Diagnosis

Idea and Motivation

Ukrainian healthcare faces a structural challenge: demand for accurate, fast, and accessible medical imaging diagnostics consistently outpaces the supply of trained specialists, particularly in regions outside major urban centres. Radiological capacity is unevenly distributed; waiting times for specialist review remain high; and diagnostic error rates in under-resourced settings are a documented concern.

This series began with a straightforward premise: machine learning approaches to medical image analysis are mature enough to provide meaningful decision-support in constrained clinical environments. The question is not whether ML can match specialist accuracy in controlled settings; the literature is clear that it can. The research question is whether these methods can be responsibly adapted to the realities of Ukrainian healthcare infrastructure: legacy imaging equipment, limited labelled datasets, and a regulatory framework not yet designed for algorithmic assistance.

Goal

The series set out to build a complete, reproducible evidence base for ML-assisted medical imaging diagnosis in Ukrainian healthcare. This means not only surveying and benchmarking ML architectures but constructing the analytical infrastructure around them: data governance frameworks, model evaluation standards, integration protocols for existing PACS systems, explainability requirements, and training curricula for healthcare professionals who will operate alongside these systems.

The goal is a self-contained research corpus that a healthcare institution, regulatory body, or research group could use as a foundation — not a collection of isolated experiments, but a structured methodology from first principles to clinical-readiness criteria.

Scope

The series covers 43 articles across six thematic phases:

Focus

The primary technical focus is on deep learning architectures for image classification and anomaly detection: convolutional neural networks (CNNs) including ResNet, EfficientNet, and DenseNet variants; Vision Transformers (ViTs) and hybrid CNN-ViT architectures; and transfer learning strategies that allow high-accuracy performance with limited domain-specific training data.

Explainable AI (XAI) is treated as a first-class requirement rather than an afterthought. Every architecture reviewed is evaluated against interpretability criteria relevant to clinical use. The Ukrainian healthcare context is maintained throughout — deployment assumptions reflect actual infrastructure, not idealised conditions.

Limitations

Scientific Value

The series makes three contributions to the field. First, it provides a structured methodology for evaluating ML medical imaging systems under resource-constrained conditions — a gap in the existing literature, which is dominated by high-resource settings. Second, it documents the Ukrainian healthcare AI landscape as a research object: regulatory gaps, infrastructure constraints, and institutional readiness factors that affect deployment. Third, it advances the application of XAI in clinical settings by developing explainability evaluation criteria tied to clinical workflow requirements rather than abstract interpretability metrics.

The ScanLab tool represents a direct research artefact: an open implementation of the series’ architectural recommendations, available for replication and extension by other research groups.

Resources

• ScanLab Diagnostic Tool→
• Stabilarity API Gateway→
• Zenodo Collection→
• GitHub Repository→
• Series DOI: 10.5281/zenodo.18752910→

Status

Complete. 43 articles published. Last updated: March 2026. No further articles are planned for this series. The research corpus is archived on Zenodo and the ScanLab tool is available for public use.

Contribution Opportunities

Researchers wishing to build on this work are encouraged to engage in the following directions:

• Empirical validation: Conduct prospective studies using the clinical protocol templates from Phase 5 with real patient cohorts under appropriate IRB oversight.
• Dataset construction: Build the Ukrainian medical imaging datasets proposed in Phase 4, following the annotation protocols and privacy frameworks described.
• ScanLab extension: Add new modalities (ultrasound, MRI) or diagnostic targets to the ScanLab architecture. The GitHub repository accepts contributions.
• Regulatory pathway: Engage with Ukrainian health regulators to develop formal approval pathways for ML-assisted diagnostic tools, using the series’ frameworks as a starting point.
• Cross-system comparison: Adapt the methodology to other post-Soviet healthcare systems to test generalisability of the Ukrainian findings.