Generative AI Unlocks New Frontiers in Personalized Medicine

The study investigates how generative AI is being used to overcome longstanding challenges in patient-specific treatment—especially those tied to data privacy, cost, and limited sample availability. By analyzing literature from Scopus and PubMed databases, the review focuses on the applications of DGMs in four core domains: clinical informatics, medical imaging, bioinformatics, and early diagnostics.

One of the most promising findings is the use of Generative Adversarial Networks (GANs) to create high-quality synthetic patient data. These datasets closely resemble real-world medical data, preserving privacy while still supporting model training and analysis. At the same time, the review underscores that large language models (LLMs), though increasingly powerful, face accuracy issues in diagnostic contexts and still require further development.