Pregnancy care draws on diverse guidelines, hospital protocols, and patient information, but this wealth of evidence also creates challenges for women seeking clear, reliable answers. Guidance can be fragmented, inconsistent, or even contradictory, leaving patients uncertain and clinicians struggling to provide consistent advice. At the same time, large language models (LLMs) and conversational AI tools are becoming increasingly common in digital health, but current systems largely provide generic responses and fail to address the complexities of conflicting information.
Existing automated approaches to guideline conflict detection are limited, often relying on model-level “black box” decisions. Such approaches suffer from several issues: they may favour whichever recommendation appears most frequently, or give higher weight to options mentioned at the beginning or end of a document. Beyond accuracy, such opaque decision-making erodes patient trust, as it provides no clear rationale for why one recommendation is preferred over another.
Our project is novel in advancing fundamental methods for: formally representing heterogeneous guideline knowledge; algorithmically detecting inconsistencies; evaluating and weighting conflicting evidence; and designing explainable reconciliation strategies suitable for patient use. By shifting the focus from simple question answering towards conflict-aware, explainable reasoning, this project addresses a key technical and clinical gap.
The overarching aim is to develop a transparent and trustworthy QA framework that can provide reliable answers even when knowledge sources disagree. Specifically, the project will (1) create representation models for pregnancy-related guidance; (2) design algorithms to detect and categorise conflicts; (3) develop evidence weighting and reconciliation strategies; and (4) integrate explainable mechanisms into a patient-facing QA system. Pregnancy care is chosen as the initial testbed given its direct impact on maternal and fetal outcomes, but the proposed methods are domain-agnostic and can be applied across healthcare more broadly.