19 citations · 28 across the 10 of their papers we have counts for
10 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…
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…
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…
Multimodal deep learning approach to predicting neurological recovery from coma after cardiac arrest
Felix H. Krones, Ben Walker, Guy Parsons +2
This work showcases our team's (The BEEGees) contributions to the 2023 George B. Moody PhysioNet Challenge. The aim was to predict neurological recovery from coma following cardiac…
Unsupervised Learning Approaches for Identifying ICU Patient Subgroups: Do Results Generalise?
Harry Mayne, Guy Parsons, Adam Mahdi
The use of unsupervised learning to identify patient subgroups has emerged as a potentially promising direction to improve the efficiency of Intensive Care Units (ICUs). By identif…
Review of multimodal machine learning approaches in healthcare
Felix Krones, Umar Marikkar, Guy Parsons +2
Machine learning methods in healthcare have traditionally focused on using data from a single modality, limiting their ability to effectively replicate the clinical practice of int…
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…
Dual Bayesian ResNet: A Deep Learning Approach to Heart Murmur Detection
Benjamin Walker, Felix Krones, Ivan Kiskin +3
This study presents our team PathToMyHeart's contribution to the George B. Moody PhysioNet Challenge 2022. Two models are implemented. The first model is a Dual Bayesian ResNet (DB…