Особливості використання EXPLAINABLE AI у біомедичній обробці зображень: прозорість та інтерпретованість моделей

• Date: 2026
• Main Authors: Пилипець, Ю.О., Ярославський, Я.І., Волосович, О.С.
• Format: Article
• Language: Ukrainian
• Published: Vinnytsia National Technical University 2026
• Subjects: Штучний інтелект (ШІ); пояснювальний ШІ (XAI); біомедична обробка зображень; біомедична обробка сигналів; глибоке навчання; машинне навчання; інтерпретованість; прозорість моделі; підтримка прийняття клінічних рішень; сегментація; класифікація; моделі на основі даних
• Online Access: https://oeipt.vntu.edu.ua/index.php/oeipt/article/view/814
• Journal Title: Optoelectronic Information-Power Technologies

Artificial intelligence (AI) has become deeply integrated into numerous scientific fields, including biomedical image and signal processing. The growing interest in this field has led to a surge in research, as evidenced by the sharp increase in scientific activity. Using large and diverse biomedical datasets, machine learning and deep learning models have transformed a variety of tasks - such as modeling, segmentation, registration, classification, and synthesis - often outperforming traditional methods. However, a major challenge remains: the difficulty of translating AI-derived results into clinically or biologically meaningful solutions, which limits the practical utility of these models. Explainable AI (XAI) seeks to bridge this gap by improving the interpretability of AI systems and offering transparent explanations for their decisions. More and more approaches are being developed to address this problem, and interest in the topic in the scientific community continues to grow.