Generative AI in Healthcare: The Promise and the Pandora’s Box

enjoy this paper as a host/guest conversation - making it simpleSummaryThis systematic review explores the application of generative AI, particularly deep generative models (DGMs) and large language models (LLMs), in revolutionizing precision medicine. The authors analyze research from Scopus and PubMed databases, focusing on how generative AI improves synthetic data generation for enhanced accuracy and privacy in clinical informatics, medical imaging, and bioinformatics. The review highlights the successes and limitations of various generative AI techniques in personalized medicine applications, such as drug response prediction and disease diagnosis. It emphasizes the need for further interdisciplinary research to address challenges like data scarcity and model generalizability, ultimately aiming to advance personalized healthcare. A significant finding is the emerging role of LLMs in supporting clinical decision-making, though their limitations are acknowledged.

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