Artificial intelligence (AI) is rapidly transforming healthcare, enabling new approaches to diagnosis, treatment optimization, and drug development. Recent advances in machine learning, multi-omics data integration, and computational modeling have expanded the role of AI beyond pattern recognition toward mechanistically informed reasoning about biological systems. This review examines emerging paradigms in AI-driven healthcare, with particular emphasis on multi-modal biomedical data integration, regulatory-grade AI systems, and new computational frameworks for predicting therapeutic efficacy and safety. We discuss the limitations of traditional predictive models in biomedical applications and highlight the emergence of AI-native reasoning architectures capable of synthesizing heterogeneous biological evidence under uncertainty. Particular attention is given to applications in drug discovery, toxicology, and regulatory science, where AI platforms can generate mechanistically interpretable insights and support decisionmaking across complex biological domains. These developments align with the evolving mission of medical AI to bridge computational science and clinical practice by producing robust, transparent, and clinically actionable knowledge.