Leveraging cutting-edge technologies has been a key priority for life science and engineeringto promote superior patient care in response to an increasingly changing clinical domain.However, one of the most notable hands-down breakthroughs is artificial intelligence (AI)and Machine Learning (ML), celebrated as a driver of an improved approach to personalizedmedicine with designs of treatment specifically based on each unique patient case. Theliterature review study thoroughly studies research from 2017 to 2024 and gives a detailedanalysis of important academic works. Around them We scrutinize the specific studies, whichare selected to most closely resonate with our core theme and in doing so examine theirmethodological underpinnings as well as findings. The scientific community is making ahuge impact in the field of AI and ML for personalized medicine through each one. Thisstudy covers a wide range of goals, approaches, and findings, all focused on the influence ofAI and ML. This highlights the profound impact that AI can have on healthcare by providingvaluable information about groundbreaking methods that have the potential to completelyimprove patient diagnosis, treatment, and care. Furthermore, it recognizes the significantachievements of scholars and innovators who are committed to enhancing healthcare bycombining AI, ML, and engineering sciences.

Conventional corrosion monitoring techniques often require costly instrumentation and direct access to structures, limiting their suitability for long-term monitoring. This study presents a machine learning feasibility study for early-stage corrosion detection using Ultra-High Frequency (UHF) Radio Frequency Identification (RFID) measurements. Machine learning algorithms were applied to a previously published RFID corrosion dataset obtained from steel specimens exposed to marine atmospheric corrosion for 0, 1, 3, and 6 months. RFID-derived features, including Analogue Identifier (AID), forward power, frequency, phase, and backscattered power, were analysed using unsupervised and supervised learning methods. For corrosion-stage discrimination, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) achieved an Adjusted Rand Index (ARI) of 1.00, a Normalised Mutual Information (NMI) score of 1.00, and a Silhouette score of 0.790. For nominal corrosion-thickness state estimation, a Random Forest regressor achieved an $R^2$ of 1.00, RMSE of 1.34 µm, and MAE of 0.21 µm under 15-fold cross-validation. Additional Leave-One-Sample-Out (LOSO) validation using readcount-based measurement-event groupings yielded an RMSE of 0.12 µm and an $R^2$ of 1.00. These results reflect nominal corrosion-thickness state estimation from repeated measurements on a single physical specimen per corrosion stage. SHAP analysis identified forward power and AID as the dominant predictive features. The results demonstrate the potential of RFID-enabled machine learning for early-stage corrosion assessment and provide a foundation for future experimental validation.