## 9 Roadmap for Future Transfer of MSK Modelling and Simulations into Clinics  
The final section of this review provides a roadmap for future applications, developments, and improvements to current MSK modelling and simulation workflows. Several application fields are quickly evolving and will likely provide a next generation of treatment pathways, which build on current applications. Specifically, surgical related applications, both in the design of prosthetics and surgical technique optimisation. Personalised representations of patients, coupled with different implant/prosthetics designs allow for virtual device testing (i.e., loading of the implant/prosthetics). These virtual testing can be performed using a combination of pre-surgical data and predictive simulations. Utilising these predictive simulations further, the effect of different surgical techniques (e.g., femoral de-rotation, tendon transfer) can be refined to achieve the optimal treatment outcomes. If these MSK modelling and simulation methods are to be extended to those requiring surgery, accuracy in modelling not only otherwise healthy individuals but those with pathologies is imperative. A specific challenge is appropriately modelling those with motor impairment, and MSK disorders. These may relate to altered muscle activation strategies, bony deformities, or altered MTU characteristics (e.g., muscle weakness). Many of these applications, particularly surgical planning, hinge on highly personalised representation of the MSK system, specifically through subject-specific MSK models. Despite a number of researchers and groups developing these subject-specific models and modelling platforms, their use especially in clinical research is scarce. This is due to the large amount of data which is required to build these models, along with the cost and time burden to collect this data. Simplification of this personalisation process is required if these models, and applications are to be realised. With respect to anatomical data (i.e., bone segmentations), the use of incomplete or sparse data to synthesize full data is a promising pathway. To this end, the use of statistical shape models (representative of the average shape of a specific population) and associated principal components (describing the shape and size variation) is becoming more common. These methods can use either incomplete segmentations [18; 19; 132] or a motion capture trial [18; 133; 134] to reconstruct personalised lower-limb geometries and assemble them into a MSK model. While becoming more common, the application of similar methods to other MSK tissues (muscles, ligaments, and cartilages) would allow for the generation of fully personalised MSK models from low-fidelity clinically feasible data. An alternative approach here is the use of population-based modelling approaches to evaluate model predictions for a range of parameters rather than aiming to accurately represent the individual patient [135; 136]. Along with the need to use low-fidelity data to create these models, low-fidelity data for executing these workflows is also required. For example, reducing the need for 3D motion capture, force plate, and EMG data will facilitate the use of MSK models in clinical practice. Using inertia measurement units (IMUs) combined with machine learning and artificial intelligence methods, joint angles, moments, and joint contact forces [137; 138; 139; 140; 141; 142; 143] can be estimated in standard clinical practice. These machine learning methods have also been applied to estimate gait speed, cadence, knee flexion angle, and common gait impairment metrics using 2D video [144]. Joint kinematics estimated using such methods could potentially serve as low-fidelity inputs into the previously discussed MSK modelling frameworks. These machine learning methods, combined with low-fidelity data (e.g., IMU, 2D video) will likely provide the most practical clinically feasible assessment of gait.