Despite major advances in biomaterial compatibility, medical implants continue to exhibit failure rates that remain high compared to engineering systems. A key reason is the limited understanding and predictive capability of biophysical integration processes, particularly bone growth and implant stability. Although high-fidelity computational models can provide detailed mechanistic insight, their computational cost and complexity prohibit large-scale studies and prevent routine patient-specific assessment in clinical practice. To overcome these limitations and accelerate predictive, patient-specific implant assessment, data-driven approaches are required that retain essential biomechanical behavior while enabling efficient evaluation across large parameter spaces. This work focuses on developing a machine learning framework to predict bone growth and assess hip implant stability, specifically migration and loosening, over time. Clinical data and synthetic finite-element numerical data are combined with biomechanical principles and constraints to ensure accurate, physically consistent, and generalizable predictions for patient-specific scenarios. Building on this predictive capability, an inverse design framework for porous implant materials is introduced to support implant stability and bone growth. Surrogate models relate microstructural features to effective mechanical and transport properties, enabling efficient design of porous architectures with implant-relevant target performance. Overall, the approach provides a scalable pathway from patient-specific assessment to implant material design, with the potential to support future clinical decision-making. REFERENCES: [1] F. Aldakheel, Y. Heider, M. Haertl´e, P. Wriggers, H. J. Maier, and M. Stiesch (2025): A Study on Cross-Applicability and Potential of Machine Learning Tools in Hip and Dental Biomechanics. Advances and Challenges in Computational Mechanics. https://doi.org/10.1007/978-3-031-93213-7 [2] P. Nguyen, Y. Heider, D. M. Kochmann, F. Aldakheel (2026): Deep learning-aided inverse design of porous metamaterials. Computer Methods in Applied Mechanics and Engineering 449 (Part A), 118499 [3] M. Haertl´e, L. Tücking, A. Derksen, M. Reulbach, E. Jakubowitz, H. Windhagen (2025): The use of an ultrasonic cement removal device in revision hip and knee arthroplasty-A matched case-control study. Journal of Experimental Orthopaedics 12:e70171