Linking Large Language Model (LLM) Analyses of Text Cervical Histology Results with Digital Vision Analysis of Histology Slide Images to Identify Biopsies with Premalignant and Malignant Lesions: Preparing for High-Risk HPV Screening Roll-Out

Neil Martinson of Perinatal HIV Research Unit of Wits Health Consortium (Pty) Limited in South Africa will apply AI-based approaches to support increased screening for high-risk HPV and its treatment to reduce the morbidity and mortality associated with cervical cancer. The scale-up of cervical HPV screening will lead to more cervical biopsies as part of follow-up care and treatment where indicated. Automated approaches for biopsy analysis would overcome the limited number of specialist pathologists otherwise required. They will train an LLM with pathology reports of cervical biopsies to extract critical wording, and they will apply computer vision for analysis of the matching digital images from each patient’s pathology slide. They will then develop a machine learning algorithm for automated reporting that accurately separates normal images from those with pathology, categorizes the pathology, and determines if the pathology extends to the biopsy margins, which indicates it is likely to recur.