Abstract: Journal of Medical Physics and Biophysics Simulations

Abstract:
Artificial Intelligence (AI) is instigating a fundamental transformation in global healthcare, offering critical solutions to rising costs, physician burnout, and the demand for personalized medicine. This paper examines the core technologies Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP), and details their profound applications across the medical ecosystem, from enhancing diagnostic accuracy in radiology and pathology to accelerating drug discovery and enabling true Precision Medicine. AI serves as a powerful augmentation tool, providing speed and scale to human clinical judgment. However, the suc cessful and equitable integration of AI faces significant hurdles, notably in the domains of data governance, algorithmic bias, regulatory uncertainty, and the imperative for Explainable AI (XAI) to build clinician trust. We conclude that realizing AI's immense potential requires a collaborative strategy focusing on transparent development, standardized data sharing through methods like Federated Learning, and policy frameworks that prioritize ethical accountability and the human-AI collaboration model.