Pathology Report Interpretation and Disease Diagnosis Using Fuzzy Logic Embedded in an Artificial Intelligence Framework: A New Paradigm for Digital Technologies.

Introduction Accurate diagnosis is essential for effective treatment in healthcare settings. Digital technologies have revolutionized medical diagnostics, particularly in pathology laboratory report analyses, by enhancing speed, quality, and precision. Diagnostic pathology has advanced significantly with digital imaging, AI algorithms, and computer-assisted techniques. Traditional machine learning methods require extensive training data, struggle with decision-making flexibility, and lack interpretability when handling new data. Methods This paper introduces a novel approach to address these challenges: a fuzzy logic-based system for interpreting pathology laboratory reports and diagnosing diseases, while considering the restrictions of engineered interpretations so that the degree of reliability of the output of the system is at par with human perception. Results The analysis highlights the impressive accuracy of the fuzzy logic-based diagnosis system, which is closely aligned with professional diagnoses. Notably, it correctly identified "normal" with an 83% probability, showcasing its potential for early disease detection. Performance evaluations indicated that the precision, recall, and