This paper examines the transformative impact of artificial intelligence (AI) in healthcare, with specific focus on medical diagnostics and drug discovery. The integration of AI technologies has revolutionized medical imaging analysis, disease diagnosis, personalized medicine approaches, and predictive analytics in patient care. Through comprehensive analysis of current implementations, this research highlights how machine learning algorithms, deep neural networks, and natural language processing have enhanced diagnostic accuracy, accelerated drug development timelines, and improved patient outcomes. The paper identifies key challenges including data privacy concerns, regulatory hurdles, integration with existing healthcare infrastructure, and the need for explainable AI systems. Despite these obstacles, AI continues to demonstrate significant potential for addressing healthcare inefficiencies, reducing costs, and enabling more precise and personalized patient care. Future directions point toward improved AI interpretability, broader clinical validation, cross-disciplinary collaboration, and ethical frameworks that balance technological advancement with patient-centered care.
Artificial intelligence; Machine learning; Deep learning; Medical imaging; Disease diagnosis; Drug discovery; Personalized medicine; Predictive analytics; Healthcare innovation; Clinical decision support systems.