  Pathology reports are rich in clinical and pathological details but are often
presented in free-text format. The unstructured nature of these reports
presents a significant challenge limiting the accessibility of their content.
In this work, we present a practical approach based on the use of large
multimodal models (LMMs) for automatically extracting information from scanned
images of pathology reports with the goal of generating a standardised report
specifying the value of different fields along with estimated confidence about
the accuracy of the extracted fields. The proposed approach overcomes
limitations of existing methods which do not assign confidence scores to
extracted fields limiting their practical use. The proposed framework uses two
stages of prompting a Large Multimodal Model (LMM) for information extraction
and validation. The framework generalises to textual reports from multiple
medical centres as well as scanned images of legacy pathology reports. We show
that the estimated confidence is an effective indicator of the accuracy of the
extracted information that can be used to select only accurately extracted
fields. We also show the prognostic significance of structured and unstructured
data from pathology reports and show that the automatically extracted field
values significant prognostic value for patient stratification. The framework
is available for evaluation via the URL: https://labieb.dcs.warwick.ac.uk/.
