Conditional Generative AI in Oncology Diagnostics

Abstract

The increasing complexity of oncology diagnostics requires advanced Clinical Decision Support Systems (CDSS) capable of integrating multimodal data. Traditional discriminative models often struggle with missing data and cross-modal dependencies. This review provides a novel, systematic analysis of conditional generative artificial intelligence (AI), including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), diffusion models and Multimodal Large Language Models (MLLMs), specifically tailored for oncological CDSS. We examine how these architectures move beyond simple prediction to learn joint data distributions, enabling robust data imputation, virtual staining, and automated clinical reporting. A central focus of this work is the assessment of translational application, identifying the gaps between experimental proof-of-concepts and clinical deployment. We address critical hurdles such as model hallucinations, domain shift, and demographic bias, providing a roadmap for biological consistency and regulatory compliance. This review highlights the transition from task-specific generators to multimodal reasoning systems. Ultimately, we argue that the integration of generative AI into diagnostic workflows is essential for precision oncology, provided that human-in-the-loop validation and uncertainty-aware inference remain central to their implementation.

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