6 papers
How sensitive do we want AI to be? Socio-communicative competencies of large language models in healthcare
Dorothee Amelung, Andrew M. Bean, Sabine C. Herpertz +4
Background. Effective clinical practice relies heavily on the socio-communicative skills of medical professionals. Large language models (LLMs) have been proposed for tasks such as…
Clinical knowledge in LLMs does not translate to human interactions
Andrew M. Bean, Rebecca Payne, Guy Parsons +8
Global healthcare providers are exploring use of large language models (LLMs) to provide medical advice to the public. LLMs now achieve nearly perfect scores on medical licensing e…
Improving In-Context Learning with Small Language Model Ensembles
M. Mehdi Mojarradi, Lingyi Yang, Robert McCraith +1
Large language models (LLMs) have shown impressive capabilities across various tasks, but their performance on domain-specific tasks remains limited. While methods like retrieval a…
Evaluating Fine-Tuning Efficiency of Human-Inspired Learning Strategies in Medical Question Answering
Yushi Yang, Andrew M. Bean, Robert McCraith +1
Fine-tuning Large Language Models (LLMs) incurs considerable training costs, driving the need for data-efficient training with optimised data ordering. Human-inspired strategies of…
Combining Hough Transform and Deep Learning Approaches to Reconstruct ECG Signals From Printouts
Felix Krones, Ben Walker, Terry Lyons +1
This work presents our team's (SignalSavants) winning contribution to the 2024 George B. Moody PhysioNet Challenge. The Challenge had two goals: reconstruct ECG signals from printo…
Do Large Language Models have Shared Weaknesses in Medical Question Answering?
Andrew M. Bean, Karolina Korgul, Felix Krones +2
Large language models (LLMs) have made rapid improvement on medical benchmarks, but their unreliability remains a persistent challenge for safe real-world uses. To design for the u…