Role of Generative Artificial Intelligence in Personalized Medicine: A Systematic Review

Area of Science:

• Computational biology and bioinformatics
• Medical informatics
• Artificial intelligence in healthcare

Background:

• Precision medicine faces challenges in data collection, cost, and privacy.
• Generative artificial intelligence (AI) can generate realistic and confidential patient data.
• AI offers a potential solution to enhance patient-centric healthcare.

Purpose of the Study:

• To systematically review the role of generative AI in personalized medicine.
• To assess the application and impact of generative AI models in tailoring treatments.

Main Methods:

• Systematic literature review following PRISMA guidelines.
• Searched PubMed, Web of Science, Scopus, CINAHL, and Google Scholar.
• Included 27 relevant studies from an initial 549 identified.

Main Results:

• Generative adversarial networks (GANs) and variational autoencoders (VAEs) were common models.
• Applications include drug response prediction, treatment effect estimation, biomarker discovery, and patient stratification.
• Generative AI shows promise for precise treatment predictions and patient-specific insights.

Conclusions:

• Generative AI holds significant potential to revolutionize personalized medicine.
• Challenges include model validation, interpretability, and bias.
• Future research needs large-scale validation with diverse datasets for clinical reliability.