Artificial intelligence and radiological imaging in oncology: state of the art and future perspectives

Abstract

Abstract Artificial intelligence (AI), in integration with the domain of radiogenomics, is transforming the perspective of oncology by bringing in imaging data with genomic profiles, to improve cancer diagnosis and treatment planning, and personalized therapies. AI-based models are more efficient for early detection of cancer, tumor-segmentation, and prediction of treatment responses, as seen in breast cancer radiomics applications. The developments have transformed the space for accuracy, workflow production, and reduced variability in image interpretation. It’s a long journey, as there are still many challenges such as data quality, data diversity, as well as ethical issues regarding privacy and trust. Regulation barriers also push waiting time for clinical adoption as these limitations must be overcome through interdisciplinary cooperation on a very strong data governance model to maximize the integration of AI in oncology. All of the conventional imaging techniques such as X-ray, CT, MRI, and PET scan are integral for staging, detecting metastasis, and evaluating treatment response. This review highlights the transformative role of artificial intelligence (AI) in oncology, focusing on its applications in radiological imaging and radiogenomics. It demonstrates how AI integrates imaging and genomic data to enhance cancer diagnosis, treatment planning, and personalized medicine. The review also examines the benefits of AI, such as improved diagnostic accuracy, workflow efficiency, and predictive capabilities for treatment responses. Additionally, it identifies goals like data quality, interpretability, ethical concerns, and regulatory hurdles, emphasizing the need for collaborative efforts to address these issues. The review provides a comprehensive overview of AI’s potential and limitations, paving the way for future advancements in oncology.

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