Explainable Artificial Intelligence in Medical Diagnosis: A Soft Computing Perspective

Abstract

ABSTRACT

The application of Artificial Intelligence (AI) in healthcare has rapidly advanced, significantly improving diagnostic accuracy, patient care, and clinical decision-making. However, the black-box nature of many AI models, especially deep learning systems, has led to concerns about their interpretability and trustworthiness in high-stakes domains like medicine. This paper explores the integration of Explainable AI (XAI) and soft computing techniques—specifically fuzzy logic and interpretable machine learning models—in medical diagnostics. These techniques aim to bridge the gap between model performance and transparency, ensuring decisions are not only accurate but also understandable by medical professionals. Through a comprehensive analysis, this paper highlights the role of fuzzy inference systems, rule-based models, and inherently interpretable machine learning algorithms, evaluating their potential to revolutionize diagnostic practices. The paper further discusses real-world case studies, advantages, limitations, and future directions to promote trustworthy and ethically aligned AI systems in healthcare.

KEYWORDS: Explainable AI, Medical Diagnosis, Fuzzy Logic, Interpretable Machine Learning, Soft Computing, Transparency in AI