Will voice-enabled AI have a role in oncology?
Progress in artificial intelligence tools is opening-up novel modalities to provide education and doctor-patient communication
Will AI reshape breast cancer screening?
While AI tools have shown promise in reducing false positives and aiding radiologists, large prospective trials are now exploring how technology should be integrated in screening programmes
AI is transforming cancer diagnosis, but systemic barriers still hold it back
A new ESMO systematic review maps AI’s growing role in pathology and oncology, as well as challenges in its implementation due to infrastructure, regulatory framework and human factors
AI applications in oncology go beyond clinical practice
While many models and tools are designed to support clinical decision-making, AI is already serving cancer research for background tasks, with some potential to educate patients too
Machine learning models predict adverse events and survival in patients receiving immunotherapy
New data supports the use of artificial intelligence for the administration of immune-check inhibitors in solid tumours
Can AI and liquid biopsies take precision oncology to the next level?
According to the 2025 ESMO TAT Honorary Awardee, Prof. Philippe Bedard, new technologies have the potential to increase the wealth of information generated from trials and registries
Education sets the basis for an AI-driven future of oncology
The deployment of artificial intelligence could lead to substantial advances in cancer care, but collaborative efforts are needed to increase trust among end users
Does artificial intelligence have a place in precision oncology?
Applications in artificial intelligence have the potential to transform precision oncology, but data quality and clinical validation of tools are still challenging
New insights into pleural mesothelioma
Studies reveal promising evidence for the more precise tailoring of immunotherapy and the role of AI in improving response evaluation
The growing role of AI in breast cancer
The rapidly increasing integration of AI into many aspects of breast cancer management calls for urgent definition of expected levels of evidence and bespoke regulatory and reimbursement processes