Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to
a readily accessible tool for cancer researchers. AI-based tools can boost research …

Artificial intelligence (AI) in oncology is advancing beyond algorithm development to
integration into clinical practice. This review describes the current state of the field, with a …

Most clinical information is encoded as free text, not accessible for quantitative analysis. This
study presents an open-source pipeline using the local large language model (LLM)“Llama …

Large language models (LLMs) are undergoing intensive research for various healthcare
domains. This systematic review and meta-analysis assesses current applications …

Abstract Background and Aims Most clinical information is encoded as text, but extracting
quantitative information from text is challenging. Large Language Models (LLMs) have …

Background Advances in precision oncology led to approval of tumour-agnostic molecularly
guided treatment options (MGTOs). The minimum requirements for claiming tumour-agnostic …

Background Osimertinib has been the standard of care in epidermal growth factor receptor
(EGFR)-mutant non-small-cell lung cancer (NSCLC). We evaluated outcomes between …

The precision oncology paradigm challenges the feasibility and data generalizability of
traditional clinical trials. Consequently, an unmet need exists for practical approaches to test …

Background & Aims Atezolizumab/bevacizumab (atezo/bev) and lenvatinib have
demonstrated efficacy as first-line therapies for hepatocellular carcinoma (HCC). However …

Background Inter-cycle delays to chemotherapy are often required to manage drug toxicity.
The impact of delays on mortality is poorly characterised. This retrospective cohort study …