A Review on Machine Learning Approaches for Magnesium Implant Corrosion Prediction

Machine learning has developed into a potent instrument for forecasting the corrosion of magnesium implants. Using 100–300 samples, Random Forest and XGBoost predict short-term in vitro corrosion metrics (i_corr, H₂, mass loss) with R² = 0.80–0.90. Graph neural networks (MGAT) obtain R² > 0.997 by encoding microstructure, while GPR provides uncertainty quantification. Experimental expenses can be reduced by up to 70% using active learning. There are still critical gaps:   ·         There isn't an open FAIR database for physiological settings (long duration, flow, proteins). ·         No machine learning model was trained using in vivo or ex vivo human bone. ·         Time to 50% mass loss (t50) and complete degradation profiles are not predicted. ·         No closed-loop validation (synthesis → in vivo testing → prediction). ·         In regulatory filings, there is no uncertainty-aware model. ·         No multi-fidelity or physics-informed machine learning.   The significance of these gaps Dynamic, protein-rich in vivo degradation cannot be predicted by models based on static, protein-free in vitro data. The biggest problem in the area is making the transition from petri dish to patient. The way ahead: Give priority to time series models, closed loop animal validation, ex vivo human bone data, and open physiological datasets. Extrapolation can be enhanced by multi-fidelity and physics-informed machine learning. ML can speed up the design of predictable, application-specific magnesium implants with integrated data infrastructure and validation, but it won't take the place of meticulous testing. Within ten years, it will be possible to create an ML-guided implant that degrades at the appropriate rate for the appropriate patient.

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