aspg-24-4518-1791482185 20261008175625 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Neutrosophic and Fuzzy Systems JNFS 2771-6430 2771-6449 2024 9 2 R. Sivasamy Department of Mathematics, Jamal Mohamed College, Tiruchirappalli–620020, Bharathidasan University, Tamil Nadu, India M. Mohammed Jabarulla Department of Mathematics, Jamal Mohamed College, Tiruchirappalli–620020, Bharathidasan University, Tamil Nadu, India S. Broumi Laboratory of Information Processing, Faculty of Science Ben M’Sik, Hassan II University, Casablanca, Morocco Medical diagnosis is an uncertainty-sensitive decision problem in which a patient state x = (x1, . . . , xm) is mapped to a candidate disease dk ∈ C using incomplete, gradual, and sometimes conflicting evidence. Fuzzy systems represent such evidence by a membership grade µ(x) ∈ [0, 1]; intuitionistic fuzzy systems use inde-pendently assigned (µ, ν) with the derived hesitation π = 1 − µ − ν; and single-valued neutrosophic systems use an independently specified triple (T, I, F ) ∈ [0, 1 ]3. This article provides a structured mathematical re-view of these models in medical diagnosis using literature available no later than 31 May 2024. The literature is synthesized by representation space, constraints, distance and similarity measures, aggregation operators, temporal structure, and diagnostic decision rules. A common embedding Φ places fuzzy and intuitionistic fuzzy states inside the neutrosophic cube Ω = [0, 1]3, making the geometric relation among the three model families explicit. Within this space, a weighted diagnostic distance is written as D(r) k =  Xm j=1 wj 3 (|Tpj − Tkj |r + |Ipj − Ikj |r + |Fpj − Fkj |r)   1/r, and the normalization residual κ = T + I + F − 1 is used to distinguish normalized states from under-specified or overlapping evidence without interpreting (T, I, F ) as probabilities. The review further devel-ops a synthesis-derived decision layer that combines distance, indeterminacy, and diagnostic margin through Sk = (1 + Dk)−1, Ck = Sk(1 − I¯p)γ , and ∆C = C(1) − C(2). The analysis indicates that fuzzy systems remain appropriate when uncertainty is predominantly gradual and rule interpretability is central, whereas neutrosophic systems are better justified when independent support, opposition, and indeterminacy must be retained. The resulting taxonomy clarifies when additional uncertainty dimensions are mathematically infor-mative and identifies calibration, metric stability, explainability, temporal modeling, and clinical validation as the principal unresolved problems. 2024 01 14 4518 https://creativecommons.org/licenses/by/4.0/ 10.54216/JNFS.090201 https://www.americaspg.com/journal/24/article/4518 https://www.americaspg.com/storage/articles/manuscripts/01M3AD3ST2N9PQ2HQZZZ5N1YNH.pdf