From symptoms to signals: Using AI for early diagnosis of ovarian cancer
About this episode
The episode discusses the use of AI and large language models for improving the early diagnosis of ovarian cancer by extracting symptoms from healthcare records.
Today, we’re speaking to Dr Garth Funston, a GP and Clinical Senior Lecturer in Primary Care Cancer Research at Queen Mary University of London. Title of paper: Using large language models to identify pre-diagnostic clinical features of ovarian cancer from healthcare records: a population-based case-control study Available at: https://doi.org/10.3399/BJGP.2025.0366 Most women with ovarian cancer present with symptoms, but many symptoms are recorded only in free text healthcare records and missed by studies and clinical decision support tools that rely on coded data. We found that using large language models (LLMs) to extract symptoms from free text records substantially increased symptom detection and strengthened associations with ovarian cancer. Incorporating LLM-extracted symptom information into research and clinical decision tools may support identification of women at higher risk of cancer and aid appropriate investigation. Transcript This transcript was generated using AI and has not been reviewed for accuracy. Please be aware it may contain errors or omissions. Speaker A 00:00:00.800 - 00:00:50.940 Hi and welcome to BJGP Interviews. I'm Nada Khan and I'm one of the…
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