METHOD FOR INTERPRETING DECISIONS MADE BY DEEP LEARNING MODELS

Abstract

The use of artificial intelligence (AI) in medical diagnostics opens new opportunities for analyzing complex medical images and optimizing diagnostic processes. One of the key challenges remains the interpretation of results obtained through AI systems, particularly in medical practice, where ensuring transparency and clarity of decision-making is critically important. This study proposes a method for visualizing and interpreting the results of cardiac disease classification based on MRI image analysis using deep learning models. The primary goal of the research is to explain AI-driven decisions in a convenient and understandable format for physicians, contributing to the reduction of subjectivity in clinical practice.
During the research, approaches were developed for visualizing key groups of medical indicators, such as heart volumes, ejection fraction, myocardial wall thickness, and volume-to-mass ratios. The study describes numerical metrics commonly used in medical practice. Fifteen key medical metrics were identified and grouped into corresponding categories for effective representation of essential medical indicators. Various visualization forms were utilized to ensure intuitive data presentation: pie charts to demonstrate ratios, the 17-segment myocardial model for analyzing wall thickness, and numerical indicators for accurately displaying volumes and ejection fraction. This approach allows physicians to quickly assess structural changes in the heart and make informed conclusions.
The proposed method aims to enhance transparency and trust in AI by providing comprehensible data representation, reducing the risks of subjective interpretation and cognitive biases. The results indicate that using such visualizations can significantly facilitate clinical decision-making, improve diagnostic accuracy, and standardize approaches to medical data analysis.