From symptoms to signals: Using AI for early diagnosis of ovarian cancer

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 Associate editors of the Journal. Thanks for listening to this podcast today.

In today's episode, we're talking to Dr. Garth Funston, who is an academic GP and clinical senior Lecturer in Primary Care Research at Queen Mary University of London.

We're here to talk about his recent paper in the BJDP which is titled Using Large Language Models to Identify Pre Diagnostic Clinical Features of Ovarian and Cancer from Healthcare Records.

So, Garth, thanks so much for talking to us again today, but I wonder, just before we get into the AI side of this paper, can you briefly explain the clinical problem you're trying to address here with ovarian cancer diagnosis in general practice?