Generative AI for Synthetic Medical Image Generation in Oncology: Addressing Data Scarcity in AI-Driven Cancer Diagnosis
Year: 2026
Type: article
Abstract: Artificial intelligence (AI) is playing an increasingly important role in cancer diagnosis and treatment. Medical imaging techniques such as MRI, CT, PET, mammography and histopathology are widely used in oncology. But AI systems need lots of medical images to train on, and many hospitals simply don't have enough good-quality cancer images. This problem is called data scarcity. Generative AI can address this challenge by producing synthetic medical images that resemble real patient images. Usually technologies such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs) and Diffusion Models are used for this purpose. This review discusses the applications of generative AI in creating synthetic images in oncology, improving AI model performance, protecting patient privacy and assisting in cancer research. The paper also addresses challenges, ethical issues, and future possibilities.
Author: Ohmini Krishnamurthy Rajendran
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W7163938670