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
Study supports radiomics in predicting immunotherapy effectiveness in advanced NSCLC
In a real-world study, multimodal artificial intelligence models outperform PD-L1 across key endpoints
AI model boosts survival prediction accuracy after neoadjuvant chemotherapy in breast cancer
A study compared the performance of different models using routinely collected clinicopathological data from women with HR-positive/HER2-negative breast cancer
Shaping the use of AI in oncology
ESMO plays a central role in developing long-lasting recommendations for a safe and effective use of artificial intelligence supporting oncology stakeholders
Artificial intelligence is expediting the development of therapeutic immunotherapies
AI-driven approaches can help to design minibinders, which may bring new T-cell receptor-based therapies closer to the clinic
Bridging the interpretation gap in cancer genomics – the next frontier for ai in oncology
Can artificial intelligence finally unlock the full potential of cancer genomics? Discover how new digital tools are closing the gap between data and clinical action in oncology.
AI-powered three-dimensional tumour pathology
Computational models can overcome current limitations of 3D analysis of tumour samples