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
A little does not go a long way to treating cancer: updated ESMO study results cause concern about stalling progress in access to medicines
The updated Antineoplastic Medicine Survey (ANMS) dataset shows that essential therapies remain out of reach for patients in most low and lower middle-income countries despite international efforts
Three new Pan-Asian-adapted guidelines at ESMO Asia Congress
The latest tumour types covered in the ESMO PAGAs are biliary tract cancers, oncogene-addicted metastatic non-small cell lung cancer and epithelial ovarian cancer
Preventing the rise in obesity-related cancers in Asia
Long-term multisectoral preventive actions that take into account local requirements are needed
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
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
Machine learning may help to uncover new immunotherapy targets
A study proposes a model that is able to learn broad aspects of cancer–immune interactions from early phase trial data