A virtual trial framework for generating clinical evidence in orthopedics demonstrated with humeral stem evaluation

Conventional clinical and experimental approaches cannot systematically evaluate orthopedic implant fixation across the full breadth of patient anatomy, bone quality, and surgical variability. Yet, digital technologies can generate clinically actionable evidence and offer a path to complement conventional preclinical and clinical evaluation of medical devices. Here, we present a virtual trial framework that integrates validated finite-element biomechanical models with machine-learning analytics to quantify implant fixation performance across realistic surgical and anatomical variability. The physics‑based finite‑element models capture bone-implant interactions under physiological loading and have been thoroughly validated against benchtop and clinical data, generating inputs for a machine‑learning layer that identifies key factors influencing primary stability and bone‑remodeling risk. We demonstrate the framework through a case‑study comparison of two humeral stem designs across more than 500 virtual surgeries, sampling patient-specific bone quality, morphology, and surgical alignment. The virtual trial reconstructs clinically relevant patterns of micromotion and stress shielding, revealing how bone density, stem sizing, and alignment jointly influence fixation outcomes. Such relationships are difficult to quantify experimentally or clinically. This digital approach establishes a scalable tool for generation of clinical evidence in orthopedics, with applications in design optimization, preoperative planning, and regulatory decision support.