Neuro-fuzzy systems in internet of medical things: a systematic review on applications, taxonomy, challenges and open...

The growing demand for real-time, adaptive, and explainable analytics in the Internet of Medical Things (IoMT) has increased interest in neuro-fuzzy systems for connected healthcare. By combining neural learning with fuzzy inference, these systems can support predictive modelling while offering rule-based reasoning structures that require explicit evaluation for interpretability, stability, and clinical usability. This paper presents a systematic review based on structured database searches, eligibility screening, quality appraisal, and narrative synthesis of 55 peer-reviewed journal articles published between January 2020 and July 2025 on neuro-fuzzy systems in IoMT and connected-health contexts. The included articles were retrieved from MDPI, SpringerLink, ScienceDirect/Elsevier, IEEE Xplore, Wiley Online Library, and Taylor & Francis, with Google Scholar used only for verification and citation tracing. An article-level, deployment-aware taxonomy was developed to classify the evidence by architecture, application, design, deployment environment, and reported metrics. Neural-network-based optimization was the most frequently reported architecture, accounting for 28 articles (50.9%), followed by hybrid neuro-fuzzy systems with 11 articles (20.0%), deep neuro-fuzzy systems with 9 articles (16.4%), and evolving neuro-fuzzy systems with 7 articles (12.7%). This distribution indicates that the evidence base is still shaped mainly by static or offline neuro-fuzzy designs, while evolving architectures for streaming, non-stationary, and patient-specific IoMT data remain comparatively underexplored. Predictive systems formed the largest application category, followed by detection-oriented systems. Deployment reporting remained limited, with 34 of the 55 included articles (61.8%) not specifying an execution environment, while only 6 articles (10.9%) reported latency or processing-time evidence. Overall, the evidence remains mainly retrospective, experimental, simulation-based, benchmark-driven, or prototype-level. The review identifies recurring gaps in online adaptability, interoperability, deployment-aware performance evaluation, and practical clinical interpretability. Although neuro-fuzzy systems show promise as adaptive models for connected-health applications, the current evidence does not yet establish routine clinical readiness or clinical effectiveness. Future work should strengthen evolving neuro-fuzzy modelling, prospective validation, deployment-aware evaluation, safety assessment, interpretability assessment, and integration with electronic health record and telemedicine workflows.