Generative AI in Healthcare: A Systematic Review of Techniques, Clinical Applications, And Ethical Challenges
Keywords:
Generative Artificial Intelligence, Healthcare Applications, Generative Adversarial Networks, Diffusion Models, Large Language Models, Medical Imaging, Drug Discovery, Clinical Decision Support, Ethical Challenges, Privacy and BiasAbstract
Generative Artificial Intelligence (AI) has emerged as a transformative paradigm in healthcare by enabling the creation of realistic synthetic data, enhancing clinical decision support, and accelerating biomedical research. Unlike traditional discriminative models, generative AI techniques—such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), diffusion models, and large language models (LLMs)—learn underlying data distributions to generate novel and meaningful outputs. This systematic review provides a comprehensive analysis of state-of-the-art generative AI techniques and their applications across key healthcare domains, including medical imaging, disease diagnosis, drug discovery, personalized treatment planning, electronic health record (EHR) synthesis, and clinical documentation. In addition, the review critically examines ethical, legal, and social challenges associated with generative AI in healthcare, such as data privacy, algorithmic bias, model interpretability, reliability, and regulatory compliance. By synthesizing findings from recent studies, this review highlights current research trends, identifies existing gaps, and discusses future research directions necessary for the responsible and effective integration of generative AI technologies into clinical practice. The insights presented aim to guide researchers, clinicians, and policymakers in leveraging generative AI to improve healthcare outcomes while ensuring ethical and trustworthy deployment.
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