Fuzzy Theory in Healthcare: A Survey, Classification, Issues, and Future Directions

Background: Fuzzy Logic (FL) is increasingly used in medical domains as it provides a robust framework for dealing with the uncertainty and subjectivity often encountered in clinical environments. With the increasing complexity of medical data and the growing need for interpretable decision-making, fuzzy systems have been widely adopted across diverse healthcare domains. Objective: This paper contributes a broad and well-structured survey of fuzzy logic techniques applied in diverse healthcare scenarios. It categorises the literature based on application domains, methodologies, data sources, and implementation tools while identifying common challenges and proposing future research directions. Methodology: A Systematic Literature Review (SLR) was conducted using five major databases: IEEE Xplore, Scopus, ScienceDirect and Web of Science. Articles published between 2017 and 2025 were filtered through established eligibility criteria to ensure relevance.The selected studies were classified into six major healthcare categories: diagnosis, monitoring, treatment recommendation, risk prediction, supporting infrastructure, and review papers. Results: The review includes over 147 studies, revealing that diagnosis is the most frequently targeted application area, with fuzzy inference systems and hybrid models being the most commonly used techniques. While FL systems show strengths in interpretability and adaptability, many suffer from a lack of clinical validation, limited datasets, and scalability concerns in real-world deployment. Discussion: The findings suggest that although FL continues to offer significant value in modelling clinical uncertainty and facilitating explainable reasoning, the field remains fragmented. There is limited standardisation across implementations, and many studies lack real-time capability or integration with hospital systems. Furthermore, most models rely on expert-driven rule design rather than data-driven tuning, limiting their adaptability to evolving medical knowledge.