A new 'Perspective' article says generative AI may help scientists read cancer’s hidden complexity across images, molecules, and clinical data, opening a possible new path to smarter diagnosis, discovery, and treatment. Perspective: Tackling the complexity of cancer with generative models. Image Credit: Antonio Marca / Shutterstock A recent Perspective article published in the journal Cell argues that generative models could help address the complexity of cancer. The “Hallmarks of Cancer” provided a framework to systemize the understanding of cancer biology. They proposed a set of principles dictating the transformation of normal cells into malignant cells and subsequent cancer progression. The hallmarks represent a reductionist framework that has unified diverse observations, yielding valuable insights. However, an intentionally simple framework cannot adequately explain the multifaceted mechanisms of cancer. Thus, complementary tools are required to capture the complex, multiscale, and multimodal nature of cancer. In this paper, the authors proposed that generative models built on advances in artificial intelligence (AI) can address the complexity of cancer. AI for Cancer Detection and Biological Understanding AI has achieved significant strides in its ability to model complex patterns over the years. Advances in learning algorithms, data availability, and processing power have led to human-level or even higher accuracy in some tasks. The applications of AI to cancer include understanding, detection, and intervention. Much of the progress in AI for cancer has been in detection. The development of deep convolutional neural networks has significantly improved image classification performance. Examples include breast cancer detection using mammographic data, skin cancer classification using lesion images, and lung cancer detection using computed tomography data. Further, many advances in understanding cancer biology have resulted from improvements in its molecular characterization. As the value of epigenomics, proteomics, transcriptomics, and other -omics measures has become clear, there is growing interest in characterizing their high-dimensional