Abstract

Generative AI is at the heart of revolutionizing healthcare, particularly in assimilating vast amounts of multi-omic data. It holds the promise of generating in-silico data sets designed from the first principles of biology, physics, chemistry, and mathematics, thus enabling biology a priori understanding. Creating trustable biological datasets is crucial for facilitating interpretations, translations, and extrapolations of biological systems. This trust revolves around the scientific validity of the conceptual models of the system, which are typically system-specific and post-processed code-wise from laboratory experiment data, including healthy conditions, perturbations, and applied diagnosis measures.Artificial Intelligence (AI) is reshaping the healthcare sector, enhancing patient health and proactively managing disease. AI models can process large datasets than human intelligence, allowing for coordinated insights and improved situational awareness. This creates a significant challenge regarding data security since all AI models require user data for development and accuracy improvements. Ethical considerations include understanding why an AI model made a certain decision, as some models act as "black boxes" that cannot predict outcomes. Generative AI, a subfield of AI focused on producing synthetic data resembling real input data, allows for the design of AI models that simulate complex systems. As AI models are exposed to more training information, these models can generate solutions resembling real environments.