Generative artificial intelligence in oncology

Purpose of review By leveraging models such as large language models (LLMs) and generative computer vision tools, generative artificial intelligence (GAI) is reshaping cancer research and oncologic practice from diagnosis to treatment to follow-up. This timely review provides a comprehensive overview of the current applications and future potential of GAI in oncology, including in urologic malignancies. Recent findings GAI has demonstrated significant potential in improving cancer diagnosis by integrating multimodal data, improving diagnostic workflows, and assisting in imaging interpretation. In treatment, GAI shows promise in aligning clinical decisions with guidelines, optimizing systemic therapy choices, and aiding patient education. Posttreatment, GAI applications include streamlining administrative tasks, improving follow-up care, and monitoring adverse events. In urologic oncology, GAI shows promise in image analysis, clinical data extraction, and outcomes research. Future developments in GAI could stimulate oncologic discovery, improve clinical efficiency, and enhance the patient-physician relationship. Summary Integration of GAI into oncology has shown some ability to enhance diagnostic accuracy, optimize treatment decisions, and improve clinical efficiency, ultimately strengthening the patient-physician relationship. Despite these advancements, the inherent stochasticity of GAI's performance necessitates human oversight, more specialized models, proper physician training, and robust guidelines to ensure its well tolerated and effective integration into oncologic practice.