Fuzzy Logic and Swarm Intelligence Algorithms in Modern Healthcare Applications

Abstract

The management of uncertainty and
imprecision is characteristic of medical information and
can complicate the decision making for the medical
doctors. Fuzzy logic, which can model imprecise
reasoning, has become an important and useful way
to tackle these challenges. However, building fuzzy
systems that include membership functions, fuzzy rules,
and inference mechanisms often requires optimization to
achieve high accuracy, robustness, and interpretability.
This paper presents a review of medical applications
that integrate fuzzy logic with optimization methods.
We summarize the theoretical foundation of fuzzy logic,
describe relevant optimization approaches, analyze key
medical applications, and present bibliometric results
from Scopus and VOSviewer.
Trends reveal an increasing fusion of fuzzy logic with
machine learning, evolutionary algorithms, and hybrid
intelligent systems driven by the needs for improved and
personalized medicine. The paper concludes with future
research directions.