Decoding Challenges using Mathematics of Fuzzy Theory in Interpretability, Shifts, Adaptation and Trust
Rashmi Singh, Aryan Chaudhary, Samrat Ray · Biomedical & Pharmacology Journal · 2025
Integrating Artificial Intelligence (AI) in medical imaging has revolutionized diagnostics by enhancing accuracy and efficiency. However, challenges related to interpretability, domain shifts, and trust hinder clinical adoption. This study introduces a fuzzy set theoretic based framework to address these issues, focusing on real-world applications. We used aa case study, where fuzzy membership grades (ranging from 0.1 to 0.9) were employed to classify tumor pixels, with a threshold of 0.6 indicating higher likelihood. Weighted average defuzzification techniques were used to integrate parameters such as pixel intensity, grayscale, and texture coefficient. Results demonstrated that pixels exceeding the threshold consistently aligned with tumor regions, validating the framework's reliability. Additionally, we explored domain shifts through feature distribution analysis between source and target datasets, highlighting the need for adaptive models. This research emphasizes the role of fuzzy sets in improving interpretability and adaptability in clinical settings, contributing to AI's trustworthiness and clinical acceptance.