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The period from 2024 to early 2026 represents a critical inflection point in biomedical research and precision medicine, driven by the deep integration of large-scale biological data with advanced artificial intelligence (AI) architectures. This era marks a transition from exploratory enthusiasm toward rigorous evaluation, where AI systems are increasingly assessed based on clinical utility, reproducibility, economic efficiency, and regulatory readiness rather than speculative potential. This review provides a comprehensive and multi-layered overview of contemporary AI applications across the biomedical landscape, spanning molecular biology, drug discovery, laboratory automation, medical imaging, genomics, clinical trials, and healthcare systems. At the molecular level, foundation models such as AlphaFold 3 and generative AI frameworks are catalyzing the emergence of “Digital Biology,” enabling accurate in silico modeling of biomolecular structures, interactions, and de novo drug design. Concurrently, self-driving laboratories are redefining experimental workflows by integrating AI agents, robotics, and real-time analytics to enhance speed, reproducibility, and scalability. In clinical and translational domains, foundation models are transforming digital pathology and medical imaging, while advances in explainable AI-particularly Concept Bottleneck Models-are addressing long-standing concerns regarding transparency and trustworthiness. In genomics and precision medicine, AI-assisted CRISPR design, variant interpretation, and game-theoretic approaches are accelerating the shift from descriptive genomics to actionable gene editing and personalized interventions. The review also highlights emerging data infrastructures, including federated learning for privacy-preserving multi-center collaboration, AI-driven clinical trial matching, and evolving regulatory frameworks from agencies such as the FDA and EMA. Special attention is given to the current state of biomedical AI development in Vietnam, illustrating both opportunities and systemic challenges in emerging healthcare ecosystems. Overall, this work positions AI not merely as a computational tool but as a collaborative scientific partner, emphasizing the necessity of aligned technological innovation, governance, ethics, and workforce training to fully realize its transformative potential in global health.