A framework to assess clinical safety and hallucination rates of LLMs for medical text summarisation.

Elham Asgari^1,2, Nina Montaña-Brown³, Magda Dubois³, Saleh Khalil³, Jasmine Balloch³, Joshua Au Yeung³, Dominic Pimenta³

¹Tortus AI, London, UK. asgelham@gmail.com.

²Guy's and St Thomas NHS Trust, London, UK. asgelham@gmail.com.

³Tortus AI, London, UK.

PubMed PMC DOI PDF

Abstract

Integrating large language models (LLMs) into healthcare can enhance workflow efficiency and patient care by automating tasks such as summarising consultations. However, the fidelity between LLM outputs and ground truth information is vital to prevent miscommunication that could lead to compromise in patient safety. We propose a framework comprising (1) an error taxonomy for classifying LLM output…

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