The paper explores the increasing integration of machine learning (ML) and artificial intelligence (AI) into modern medicine, particularly for predictive modeling in healthcare. It highlights how these technologies aim to reduce subjectivity in diagnoses and forecasts, offering potential benefits like early disease detection and resource optimization. However, the source also critically examines the significant challenges and risks associated with their uncontrolled deployment, such as the potential for data contamination within electronic health records (EHRs), which can degrade model performance over time. The author emphasizes the need for transparency, robust evaluation methods, and careful documentation of AI's influence on clinical decisions to ensure long-term reliability and accountability in healthcare. The text ultimately suggests that while AI offers transformative potential, uncontrolled implementation risks sacrificing data integrity for short-term gains.
