JMIR Publications

ABSTRACT

Background:

Fuzzy logic has been progressively investigated as a viable alternative to traditional statistical and machine learning methods in healthcare modeling, especially in environments marked by uncertainty, non-linearity, and missing information. Although its use in prediction, classification, and risk stratification is well established, its application to explicit causal inference remains limited, varied, and methodologically premature.

Objective:

This systematic review aimed to examine how fuzzy logic frameworks have been used to address causal questions in healthcare, focusing on their methodological characteristics, comparative performance, and degree of integration with formal causal inference approaches.

Methods:

A systematic search across six databases (PubMed, Web of Science, ScienceDirect, SpringerLink, Scopus, and IEEE Xplore) identified peer-reviewed studies published between 2014 and 2025 that applied fuzzy modelling in healthcare settings with explicit or implicit causal objectives. The review adhered to PRISMA 2020 guidelines and employed a modified PICO framework for study selection. Data were extracted on healthcare domain, fuzzy method, comparator use, and causal framing. Risk of bias was evaluated using the Joanna Briggs Institute (JBI) Checklist and the PROBAST+AI tool, according to study design.

Results:

Thirty-seven studies met the inclusion criteria. The most frequently applied approaches were Fuzzy Inference Systems, Fuzzy Cognitive Maps, and neuro-fuzzy models, with applications spanning infectious diseases, cancer, cardiovascular health, mental health, and occupational health. Fourteen studies included comparator models; among these, five reported superior performance of fuzzy approaches, three showed comparable results, and six lacked sufficient detail for robust comparison. Only two studies explicitly implemented formal causal inference frameworks, while most relied on predictive or associative modelling with implicit causal assumptions. Overall risk of bias was moderate to high.

Conclusions:

Fuzzy logic offers interpretability and flexibility well suited to complex healthcare problems, yet its application to explicit causal inference remains fragmented. Greater methodological transparency, systematic benchmarking, and integration with formal causal designs—such as counterfactual and target trial frameworks—are required to establish fuzzy logic as a robust paradigm for causal inference in healthcare. Clinical Trial: This systematic review was prospectively registered in the PROSPERO database (CRD420251044493)