Machine Learning for Predictive Modeling and Personalized Treatment in Magnesium-based Biomedical Applications

This paper explores the integration of machine learning (ML) techniques with magnesium-based biomedical applications, focusing on predictive modeling and personalized treatment strategies. Magnesium's biocompatibility and biodegradability make it an attractive material for various medical applications, including implants and drug delivery systems. However, optimizing its use for individual patients remains challenging. In this paper it is proposed that ML can bridge this gap by enabling more accurate predictions of treatment outcomes and facilitating personalized approaches. Through a comprehensive review of existing literature and analysis of case studies, it is demonstrated that how supervised learning, unsupervised learning, and deep learning techniques can be applied to improve patient outcomes in magnesium-based treatments. The paper also addresses challenges such as data privacy and model interpretability, proposing solutions for integrating ML into clinical workflows. Our findings suggest that the synergy between ML and magnesium-based biomedical applications has the potential to significantly advance personalized medicine, opening new avenues for research and clinical practice.