## 9 Roadmap for Future Transfer of MSK Modelling and Simulations into Clinics
Likewise, collection of a subset of muscle activity may be sufficient to provide an estimated of the muscle coordination strategy without the need of collecting a large number of EMG channel. These data can then be used to improve estimates of muscle forces and activations. Similar to the previously mentioned statistical shape modelling methods, using a subset of collected EMG, muscle activations can be reconstructed through the use of muscle synergy methods [145; 146; 147; 148; 149]. However, the use of these low-fidelity data methods, combined with simplified MSK modelling for simulation purposes still needs to be further explored in a research context prior to its adoption in clinics. In this respect, the adoption of low-fidelity input data for both model creation and execution should however be carefully coupled with both uncertainty and sensitivity analyses to evaluate the set of parameters that have a large effect on simulation results, and as such should be accurately defined. Conversely parameters which have little effect on simulation results can be defined using low-fidelity and less accurate methods, hence reducing the data collection burden. Such analyses have already been performed showing the influence of mass and inertial properties [150]. joint location estimations [123; 150], bone-joint geometry and alignment [84; 151], muscle parameters [38; 152; 153], and muscle redundancy formulation [111] on various simulation results including joint moments, muscle force estimates, and joint contact forces. Despite these previous studies, these forms of sensitivity and uncertainty analyses have not been systematically completed on a single dataset, meaning the ranking of these parameters, in terms of simulation sensitivity is difficult to interpret. Additionally, previous research has typically investigated the effect on a single or limited simulation results. As the importance of a specific parameter is likely dependent on the output of interest, extensive sensitivity analysis on an output by output basis will provide the ideal balance between data collection burden and simulation validity. In conclusion, the integration of clinically collected data (i.e., 3D motion capture) with MSK models can provide estimates of numerous clinically relevant parameter related to MSK loading which cannot be estimated using commercially available clinical gait analysis suites. Estimates of these biomarkers can enhance several clinical treatment pathways including rehabilitation, patient stratification, and disease aetiology investigation. Additionally, optimisation of endoprosthesis, prosthetics, assistive devices, and exoskeletons design and controls can be performed using novel predictive simulations. Although numerous clinical applications exist, the adoption of these models and methods are not common, hindered by the specialist knowledge requirements for the execution of workflows and interpretation of results. A specific focus should be increasing the access and useability for non-expert users. This should be accompanied by information regarding appropriate data collection to ensure quality simulation results. Simulation result reports, which give the clinician and patient easy to interpret answers to clinically relevant questions, should be made a priority if these modelling and simulation methods are to be widely adopted. Further integration of MSK modelling and simulations as a clinical tool cannot be achieved by MSK modelling and simulation experts alone, but requires an effort from an interdisciplinary team including modelling and simulation experts and clinicians in order to adequately balance the clinical expectations with the constraints of the modelling and simulation approaches and clinically feasible data. All authors ([PERSON], [PERSON], [PERSON] and [PERSON]) contributed to the conceptualization and structuring of the work, writing of the original draft, and writing and review of the revised manuscript. All authors have read and agreed to the published version of the manuscript.