## 7 Closing Remarks
The computational cost of iterative inverse methods is often addressed by simplifications of the forward problem, the use of surrogate models for early optimization stages, utilization of more efficient iterative optimization methods, and the use of parallel computing. Furthermore, the ongoing increase of computational power may allow the solution of complex problems that escapes the reach of current technology. Similarly, it is reasonable to expect that medical imaging technology will continue to evolve, making them more readily available in healthcare practice. The development of data-driven techniques for the support of clinical decision making and treatment planning could also motivate the implementation of image-based kinematics in routine health care. Inverse modeling is just one of many patient-specific techniques that have been proposed as a useful support for clinical practice. Machine learning has been increasingly explored in the last two decades for incorporation into the new field of precision medicine [307]. This technique consists of training decision-making algorithms with annotated large datasets, which when combined with the application of statistical principles, can return valuable evidence-based information from raw clinical data [308]. The main advantage of machine learning techniques is that once the algorithm has been trained, results can be obtained in short times with low associated computational cost. However, the outcomes are highly dependent on the quality of the annotated dataset used for training, as they are not the result of a physiology-based simulation but on statistical probabilities calculated from collected evidence. Thus, this approach can potentially fail if unique or unexpected conditions are presented. An additional advantage of simulation-based techniques is their predictive capabilities. Founded on physical and physiological principles, patient-specific inverse problems can be coupled to mechanobiology-inspired growth and remodeling models to potentially predict the progression of diseases and/or the effect of treatments [106]. In conclusion, image-based inverse modeling is a promising quantitative tool to generate and analyze clinically relevant physiological data through a non-invasive approach with the ultimate goal of providing improved patient-specific diagnostic and prognostic assessments of diverse cardiovascular diseases in order to improve outcomes, reduce costs, and increase the quality of life. Conceptualization, J.H.B. and J.S.S. ; methodology, J.H.B., S.K.S. and J.S.S. ; data collection, J.H.B., U.T.T. and J.S.W. ; data analysis and interpretation, J.H.B., U.T.T., J.S.W. and J.S.S. ; writing--original draft preparation J.H.B. ; writing--review and editing J.H.B., S.K.S., J.S.W., U.T.T. and J.S.S. ; visualization J.H.B. and U.T.T. ; supervision J.S.S. ; project administration, J.S.S. ; funding acquisition, J.H.B., S.K.S. and U.T.T. All authors have read and agreed to the published version of the manuscript. This work was supported by the American Heart Association and the D.C. Women's Board (award number 834649/2021), the American Heart Association (award number 20 PRE35210730/2020), and the National Institute of Health (grant number NIH NHLI K23 HL135352). We thank [PERSON], [PERSON], [PERSON], [PERSON] and the VCU College of Engineering DURI Dean's Undergraduate Research Initiative Program. The authors declare no conflict of interest.