Generative Artificial Intelligence in biology and medicine is emerging as a powerful approach for understanding complex biological systems and improving healthcare outcomes. This paper examines the role of generative models such as generative adversarial networks, variational autoencoders, and large language models in applications including drug discovery, protein structure prediction, medical imaging, and personalized medicine. In addition, the concept of PM GenAI, referring to predictive and personalized medicine powered by generative artificial intelligence, is explored as a key advancement that integrates patient specific data with generative modeling to support individualized diagnosis and treatment strategies. Generative AI systems are capable of learning from large scale biological and clinical datasets to produce realistic molecular structures, simulate biological processes, and enhance clinical decision making. In drug discovery, these models accelerate the identification of candidate compounds with desired therapeutic properties, significantly reducing development time and cost. In medical imaging, generative techniques improve image quality, enable robust data augmentation, and support early and accurate disease detection. Furthermore, applications in genomics and synthetic biology allow for the design of DNA sequences and prediction of functional biological outcomes. The paper also addresses important challenges including data bias, limited interpretability, ethical considerations, and regulatory constraints. The integration of GenAI introduces additional concerns related to data privacy and fairness in personalized healthcare systems. Addressing these challenges requires rigorous validation, transparent model design, and interdisciplinary collaboration between computational scientists, biologists, and clinicians. Overall, generative artificial intelligence and present significant opportunities to transform biological research and medical practice while necessitating careful oversight to ensure safe and equitable use.
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