AI may be key to streamline patient allocation to clinical trials
As a proof of concept, an AI-powered large language model matching platform shows promise in patient selection
As a proof of concept, an AI-powered large language model matching platform shows promise in patient selection
While synthetic real-world data may help overcome current challenges in clinical trials, it also introduces new complexities
Findings from latest research reflect emerging opportunities in oncology to refine treatments by challenging current paradigms of care
Findings from four studies provide further evidence on the potential of AI-driven pathology, imaging and multiomic data to guide clinical decision-making
Recent findings show digital tools have the potential to improve the monitoring of symptoms and psychological aspects, but they should be integrated within routine clinical practice
The ELCAP statements, developed by a group of international experts, help oncology stakeholders recognise the opportunities and the risks of these artificial intelligence systems by categorising them into three types
Study findings reveal that good thymic health, based on an AI-derived algorithm, is associated with improved survival after immunotherapy in different tumour types
Leveraging artificial intelligence and digital pathology, oncology is entering a transformative stage where advanced image analysis and AI-driven biomarkers enable personalised cancer treatment decisions.
Pathology is going digital. More than 50 AIs cleared for diagnostic use are now available. This article summarises the status quo and gives an outlook of upcoming emerging technologies.
An interview with ESMO President Prof. Fabrice André underscores the importance for any medical oncologist of developing artificial intelligence literacy
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