7 papers
A Calibrated Memorization Index (MI) for Detecting Training Data Leakage in Generative MRI Models
Yash Deo, Yan Jia, Toni Lassila +5
Image generative models are known to duplicate images from the training data as part of their outputs, which can lead to privacy concerns when used for medical image generation. We…
WER is Unaware: Assessing How ASR Errors Distort Clinical Understanding in Patient Facing Dialogue
Zachary Ellis, Jared Joselowitz, Yash Deo +7
As Automatic Speech Recognition (ASR) is increasingly deployed in clinical dialogue, standard evaluations still rely heavily on Word Error Rate (WER). This paper challenges that st…
MATRIX: Multi-Agent simulaTion fRamework for safe Interactions and conteXtual clinical conversational evaluation
Ernest Lim, Yajie Vera He, Jared Joselowitz +9
Despite the growing use of large language models (LLMs) in clinical dialogue systems, existing evaluations focus on task completion or fluency, offering little insight into the beh…
Metrics that matter: Evaluating image quality metrics for medical image generation
Yash Deo, Yan Jia, Toni Lassila +5
Evaluating generative models for synthetic medical imaging is crucial yet challenging, especially given the high standards of fidelity, anatomical accuracy, and safety required for…
The BIG Argument for AI Safety Cases
Ibrahim Habli, Richard Hawkins, Colin Paterson +4
We present our Balanced, Integrated and Grounded (BIG) argument for assuring the safety of AI systems. The BIG argument adopts a whole-system approach to constructing a safety case…
The case for delegated AI autonomy for Human AI teaming in healthcare
Yan Jia, Harriet Evans, Zoe Porter +5
In this paper we propose an advanced approach to integrating artificial intelligence (AI) into healthcare: autonomous decision support. This approach allows the AI algorithm to act…