ML for Medical Imaging Diagnosis
Global medical AI has exploded with 1,200+ FDA-approved devices, yet 81% of US hospitals have no AI adoption. Article #2 maps the adoption paradox, regional variation, success rates by use case, and the critical barriers—with lessons for Ukrainian healthcare.
Launching a 12-week research program to build a practical framework for ML-augmented medical image diagnosis in Ukrainian healthcare. Article #1 establishes methodology, introduces Stabilarity Hub ecosystem, and outlines the path from research to ScanLab implementation.
A comprehensive review of machine learning in medical image analysis, examining which ML techniques apply at each diagnostic stage, evidence-based best practices for doctor-AI collaboration, and unique conclusions on reducing diagnostic errors.