47 citations · 67 across the 10 of their papers we have counts for
11 papers
TCMClinicalReason-Bench: Can Language Models Reason from Pathogenesis to Prescription over Real-World Clinical Cases?
Jirui Dai, Chenkai Zhang, Yan Jia +4
Large language models (LLMs) can generate clinical narratives that are insufficiently grounded in patient-specific evidence. In traditional Chinese medicine (TCM), errors can propa…
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…
Upstream and Downstream AI Safety: Both on the Same River?
John McDermid, Yan Jia, Ibrahim Habli
Traditional safety engineering assesses systems in their context of use, e.g. the operational design domain (road layout, speed limits, weather, etc.) for self-driving vehicles (in…
The Role of Explainability in Assuring Safety of Machine Learning in Healthcare
Yan Jia, John McDermid, Tom Lawton +1
Established approaches to assuring safety-critical systems and software are difficult to apply to systems employing ML where there is no clear, pre-defined specification against wh…
Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS)
Richard Hawkins, Colin Paterson, Chiara Picardi +3
Machine Learning (ML) is now used in a range of systems with results that are reported to exceed, under certain conditions, human performance. Many of these systems, in domains suc…
A Framework for Assurance of Medication Safety using Machine Learning
Yan Jia, Tom Lawton, John McDermid +2
Medication errors continue to be the leading cause of avoidable patient harm in hospitals. This paper sets out a framework to assure medication safety that combines machine learnin…