Clean & Clear: Feasibility of Safe LLM Clinical Guidance

Background: Clinical guidelines are central to safe evidence-based medicine in modern healthcare, providing diagnostic criteria, treatment options and monitoring advice for a wide range of illnesses. LLM-empowered chatbots have shown great promise in Healthcare Q&A tasks, offering the potential to provide quick and accurate responses to medical inquiries. Our main objective was the development and preliminary assessment of an LLM-empowered chatbot software capable of reliably answering clinical guideline questions using University College London Hospital (UCLH) clinical guidelines. Methods: We used the open-weight Llama-3.1-8B LLM to extract relevant information from the UCLH guidelines to answer questions. Our approach highlights the safety and reliability of referencing information over its interpretation and response generation. Seven doctors from the ward assessed the chatbot's performance by comparing its answers to the gold standard. Results: Our chatbot demonstrates promising performance in terms of relevance, with ~73% of its responses rated as very relevant, showcasing a strong understanding of the clinical context. Importantly, our chatbot achieves a recall of 0.98 for extracted guideline lines, substantially minimising the risk of missing critical information. Approximately 78% of responses were rated satisfactory in terms of completeness. A small portion (~14.5%) contained minor unnecessary information, indicating occasional lapses in precision. The chatbot' showed high efficiency, with an average completion time of 10 seconds, compared to 30 seconds for human respondents. Evaluation of clinical reasoning showed that 72% of the chatbot's responses were without flaws. Our chatbot demonstrates significant potential to speed up and improve the process of accessing locally relevant clinical information for healthcare professionals.

Multimodal Search in Chemical Documents and Reactions

We present a multimodal search tool that facilitates retrieval of chemical reactions, molecular structures, and associated text from scientific literature. Queries may combine molecular diagrams, textual descriptions, and reaction data, allowing users to connect different representations of chemical information. To support this, the indexing process includes chemical diagram extraction and parsing, extraction of reaction data from text in tabular form, and cross-modal linking of diagrams and their mentions in text. We describe the system's architecture, key functionalities, and retrieval process, along with expert assessments of the system. This demo highlights the workflow and technical components of the search system.