Generative Artificial Intelligence Poised to Revolutionize Personalized Medicine, New Systematic Review Reveals

Breakthrough study suggests Artificial Intelligence-powered models could benefit over 150 million patients worldwide and save billions in healthcare costs.
Boston, MA - A newly published systematic review is shining a spotlight on how generative artificial intelligence (AI) could radically transform personalized medicine, enhancing healthcare outcomes for millions of patients while cutting billions in treatment costs. The peer-reviewed paper, titled "Role of Generative Artificial Intelligence in Personalized Medicine: A Systematic Review," was released by a collaborative team of international researchers.

The authors - Aashish Mishra, Anirban Majumder, Dheeraj Kommineni, Chrishanti Anna Joseph, Tanay Chowdhury, and Sathish Krishna Anumula - systematically reviewed 27 key studies, highlighting how generative AI models, especially generative adversarial networks (GANs) and variational autoencoders (VAEs), are advancing drug response prediction, treatment effect estimation, and biomarker discovery.

"This paper marks a pivotal moment in the evolution of patient-specific medicine," said Thomas Berger, Senior Journalist at Alpine Vision Media, "The ability of AI to create high-fidelity, confidential synthetic patient data opens the door to highly accurate, privacy-preserving solutions across multiple disease domains." According to the review, over 150 million people globally - including those suffering from cancer, autoimmune disorders, rare diseases, and chronic conditions - could benefit from AI-augmented, precision-targeted therapies. In the United States alone, early implementation of such models could save up to $45 billion annually by reducing misdiagnoses, avoiding ineffective treatments, and lowering readmission rates.

The authors identified GANs as the most commonly used models, featured in 16 of the reviewed studies, due to their strength in simulating realistic and diverse patient data without compromising confidentiality. VAEs, highlighted in seven studies, were praised for their capacity to uncover hidden clinical patterns, making them ideal tools for patient stratification and therapeutic guidance. Despite its promise, the review underscores the challenges ahead, including the need for improved model validation, algorithmic transparency, and minimizing data bias. It calls for large-scale, multi-center trials to ensure these tools are broadly applicable and equitable. "This research sets a new benchmark for evaluating clinical AI tools," added Berger, "It also places the spotlight firmly on the need for ethical frameworks that can keep pace with this technological acceleration."

The findings build on a growing body of international work in AI and healthcare, echoing contributions by thought leaders such as Professor Enrico Coiera of Australia, known for his work in digital health ecosystems, and Dr. Adeola Olayemi of Nigeria, a pioneer in using AI to improve maternal health outcomes. Together with the present study, these efforts signal a broader shift toward intelligent, patient-centric healthcare on a global scale.

As the healthcare industry enters a new era, this paper offers a comprehensive guide for policymakers, health systems, and tech developers to responsibly integrate generative AI in ways that maximize clinical value while safeguarding patient trust.

Citation: Mishra A, Majumder A, Kommineni D, et al. (April 15, 2025) Role of Generative Artificial Intelligence in Personalized Medicine: A Systematic Review. Cureus 17(4): e82310. DOI 10.7759/cureus.82310

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