Generative AI applications in personalized medicine have created a
comprehensive opportunity for more pinpoint and specialized patient outcome
prediction. Medical practitioners can utilize highly developed generative models, such
as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), to
create complex patient datasets, model potential treatment outcomes, and identify
intricate patterns in multi-omics data. Generative AI models are particularly helpful in
rare and complex situations where the conventional approach is not fruitful. These
models can improve the accuracy of predictions related to disease and treatment
effectiveness. One additional feature of Generative AI models is to protect patient
privacy through the creation of synthetic data. Generative AI augments existing data
and creates new datasets for patient care outcomes.
This chapter focuses on improving accuracy and the application of Generative AI in
drug discovery, highlighting the potential of Generative AI for clinical decision-making
and personalized treatment planning. This chapter covers the latest trends, including the
integration of Generative AI with electronic health records and real-time monitoring
devices, as well as the ethical issues—such as preventing biases and ensuring data
security—that come with it. Generative AI has the potential to transform the current
healthcare system and deliver a more accurate, patient-centric approach to medical
outcomes. This chapter focuses on the latest technology in the Healthcare sector and
also covers the current challenges in modern drug discovery.
Keywords: AI-driven healthcare innovation, Generative artificial intelligence, Predictive modeling in healthcare, Patient outcome prediction, Real-time health monitoring.
