Volume 16, Issue 6 And S7 (Artificial Intelligence- In Press 2026)
J Research Health 2026, 16(6 And S7): 6-6 |
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Shafaei Bajestani N, Zare A. Type-3 Fuzzy Logic in Healthcare: A Comprehensive Review. , J Research Health. 2026; 16 (6 and S7) :6-6
URL: http://jrh.gmu.ac.ir/article-1-3103-en.html
URL: http://jrh.gmu.ac.ir/article-1-3103-en.html
1- Department of Electrical Engineering, Gon.C. , Islamic Azad University, Gonabad, Iran; , narges.shafaei@gmail.com
2- Research Center of Intelligent Technologies in Electrical Industry, Gon.C. , Islamic Azad University, Gonabad, Iran
2- Research Center of Intelligent Technologies in Electrical Industry, Gon.C. , Islamic Azad University, Gonabad, Iran
Abstract: (21 Views)
Background: Type-3 fuzzy logic systems (T3FLS) are the latest advancements in fuzzy set theory aimed at tackling the intricate and multilayered uncertainties that exist in medicine and healthcare. Compared to type-1 and type-2 fuzzy models, type-3 systems offer more refined cognitive hierarchies and interval-based representations, which provide greater and more flexible reasoning possibilities amid ambiguous, noisy, or incomplete clinical scenarios.
Methods: For the systematic review, the recent applications of T3FLS in medicine and healthcare were collated. This was done by running searches in PubMed, Scopus, IEEE Xplore, and Web of Science, with no restrictions on chronological limits. Eligible studies were those that utilized type-3 fuzzy logic within the context of diagnostic decision-making, treatment optimization, or predictive algorithms. Due to the diversity in study design and application domains, the findings were synthesized qualitatively. In total, approximately 13 studies were examined in detail.
Results: The literature review shows T3FLS is better than type-1 and type-2 variants at handling uncertainty and advancing decision-making support within the medical field. Described use cases include diagnostic imaging, clinical risk evaluation, and disease classification. The increased accuracy and robustness relative to noisy datasets overshadow the ability to address incomplete datasets. On the other hand, insights on the balance of computational burden and analytical framework standardization, and especially, model interpretability remain challenges across the reviewed documents.
Conclusion: The advanced uncertainty management capabilities of type-3 fuzzy logic systems provides a strong basis for enhancing intelligent healthcare decision support systems. The clinical field, however, likely will not see such systems until targeted empirical studies and methodological standardization is conducted. A promising direction for more flexible, explicable hybrid models that better meet the demands of the healthcare setting would be the integration of T3FLS and machine learning. The results of the reviewed studies indicate that T3FLS can diagnose diseases with higher accuracy compared to earlier fuzzy models.
Methods: For the systematic review, the recent applications of T3FLS in medicine and healthcare were collated. This was done by running searches in PubMed, Scopus, IEEE Xplore, and Web of Science, with no restrictions on chronological limits. Eligible studies were those that utilized type-3 fuzzy logic within the context of diagnostic decision-making, treatment optimization, or predictive algorithms. Due to the diversity in study design and application domains, the findings were synthesized qualitatively. In total, approximately 13 studies were examined in detail.
Results: The literature review shows T3FLS is better than type-1 and type-2 variants at handling uncertainty and advancing decision-making support within the medical field. Described use cases include diagnostic imaging, clinical risk evaluation, and disease classification. The increased accuracy and robustness relative to noisy datasets overshadow the ability to address incomplete datasets. On the other hand, insights on the balance of computational burden and analytical framework standardization, and especially, model interpretability remain challenges across the reviewed documents.
Conclusion: The advanced uncertainty management capabilities of type-3 fuzzy logic systems provides a strong basis for enhancing intelligent healthcare decision support systems. The clinical field, however, likely will not see such systems until targeted empirical studies and methodological standardization is conducted. A promising direction for more flexible, explicable hybrid models that better meet the demands of the healthcare setting would be the integration of T3FLS and machine learning. The results of the reviewed studies indicate that T3FLS can diagnose diseases with higher accuracy compared to earlier fuzzy models.
Type of Study: Review Article |
Subject:
● Artificial Intelligence
Received: 2026/06/5 | Accepted: 2026/10/5 | Published: 2026/11/11
Received: 2026/06/5 | Accepted: 2026/10/5 | Published: 2026/11/11
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