Exploiting HER2 tumour heterogeneity with digital oncology
Multi-regional modelling reveals new ways to personalise treatments in gastric and breast cancers
Multimodal AI biomarkers: from biology to patient stratification
Artificial intelligence is enabling a new class of biomarkers by integrating histology, molecular data, imaging, and clinical records to generate scalable, biologically grounded insights for precision oncology.
AI-driven multimodal strategy improves risk stratification in early breast cancer
Integrating digital pathology with genomic and clinical data may enhance prediction of late recurrence and refine selection for adjuvant treatment intensification in HR-positive, HER2-negative disease
Customised app shows promise for monitoring prostate cancer patients in real-time
A pilot study reports high levels of acceptance and compliance despite an older patient population
Why AI-powered trial matching alone will not fix oncology trial recruitment
AI-powered trial matching helps identify relevant trials faster, but recruitment often fails beyond the algorithm. Limited cross-site trial discovery, outdated recruitment information and fragmented referral pathways continue to prevent potentially eligible patients from participating in relevant trials.
Can AI chatbots effectively support patients with cancer during treatments?
Early findings suggest potential benefits in symptom monitoring, but highlight usability challenges and added burden on clinical workflows
Redesigning oncology clinical trials with agentic AI
Semi-autonomous and autonomous foundation models are helping cancer research and anticancer drug development
A driving force to accelerate drug development
Fostering academic clinical research is one of the ESMO’s priorities, and artificial intelligence may be a major driver
Research unlocks insights into lung cancer evolution from electronic medical records
A domain-specific natural language processing (NPL) pipeline showed strong performance in extracting clinically meaningful information from diverse clinical documents of patients with non-small cell lung cancer
Trustworthy and explainable AI – a key to equitable oncology?
Mitigating research biases by rigorous data analysis and validation can pave the way for a wider adoption of artificial intelligence in cancer care