## 4 Discussion
The second major limitation is the elevated reporting time that most of the identified techniques require to produce the numerical results. In common clinical practice, often, clinicians deal with time-sensitive situations, where the mortality rate increases rapidly [9] (e.g., AD) and there is no option to wait for days or weeks for results. [PERSON] et al. [52] also have pointed out the high computational cost required to perform viable simulations as one of the greatest bottlenecks in clinical implementation. Mesh generation is often one of the most time-consuming steps in grid-based models such as FEM or FVM. Furthermore, in 2D and 3D simulations, the calculation itself is also a highly time-consuming step due to the elevated amount of performed calculations and domain subdivisions. Moreover, these models regularly deal with convergence-related problems and the patient-to-patient adaptability usually requires time-consuming calibration processes. In addition, regarding the reporting time, as expected, we found evidence that computational effort grows with model complexity. For instance, laminar simulations take less time to compute results than turbulent simulations. FSI simulations can lead to a 15-fold increase in computational time, as reported by [PERSON] et al. [108]. This also enhances the need for studying the equilibrium between model complexity and simulation time. On this topic, there also have been great efforts to improve the efficiency of numerical models. [PERSON] et al. [115], [PERSON] et al. [43] and [PERSON] et al. [32] explored the application of structured grids, which are known for reducing computational effort as it requires fewer elements to achieve convergence. [PERSON] et al. [60] and [PERSON] et al. [61] implemented meshless models and reported significant reductions in computational effort. ROM was also suggested as an efficient technique to estimate haemodynamics accurately and reduce reporting time [48, 53, 117, 118]. [PERSON] et al. [93] proposed a new optimisation method (Multi-Resolution Direct Search) designed to reduce the high computational effort inherent to inverse methodologies. Moreover, [PERSON] et al. [29] suggested the use of GPU-based (instead of CPU) methods such as SPH [59, 60]. Lastly, the third major limitation and possibly the most relevant is the lack of extensive clinical trials that prove the efficiency of these tools to precisely predict the development of severe pathological conditions. Studies on large patient cohorts are still needed and essential to the implementation of numerical models in clinical guidelines. Only 12% of the works analysed in this review performed numerical validation with patient-specific in vivo data and the majority (76%) did not present a meaningful validation process, which also contributes to the lack of proof of numerical modelling efficiency. To conclude, it is pointed out that the present systematic review was limited to only one electronic database, with the exclusion of MSc and PhD theses. In future works, the authors will seek to further understand the biomechanics of the most common aortic diseases ( ATAA, AAA and AD) and explore which rheology and turbulence models, BC and numerical techniques and approaches produce better results.