1 citations · 4 across the 10 of their papers we have counts for
11 papers
SafeLLM: Extraction as a Hallucination-Resistant Alternative to Rewriting in Safety-Critical Settings
Julia Ive, Felix Jozsa, Evridiki Georgaki +6
Large language models (LLMs) are increasingly used to access organisational documentation, including standard operating procedures (SOPs), HR policies and institutional guidelines.…
Building a Silver-Standard Dataset from NICE Guidelines for Clinical LLMs
Qing Ding, Eric Hua Qing Zhang, Felix Jozsa +1
Large language models (LLMs) are increasingly used in healthcare, yet standardised benchmarks for evaluating guideline-based clinical reasoning are missing. This study introduces a…
Context-Emotion Aware Therapeutic Dialogue Generation: A Multi-component Reinforcement Learning Approach to Language Models for Mental Health Support
Eric Hua Qing Zhang, Julia Ive
Mental health disorders impose a substantial global socioeconomic burden. While large language models (LLMs) offer 24/7, non-judgmental interactions to address this gap, pretrained…
Development and Evaluation of HopeBot: an LLM-based chatbot for structured and interactive PHQ-9 depression screening
Zhijun Guo, Alvina Lai, Julia Ive +5
Static tools like the Patient Health Questionnaire-9 (PHQ-9) effectively screen depression but lack interactivity and adaptability. We developed HopeBot, a chatbot powered by a lar…
Developing and Evaluating an AI-Assisted Prediction Model for Unplanned Intensive Care Admissions following Elective Neurosurgery using Natural Language Processing within an Electronic Healthcare Record System
Julia Ive, Olatomiwa Olukoya, Jonathan P. Funnell +7
Introduction: Timely care in a specialised neuro-intensive therapy unit (ITU) reduces mortality and hospital stays, with planned admissions being safer than unplanned ones. However…
Clean & Clear: Feasibility of Safe LLM Clinical Guidance
Julia Ive, Felix Jozsa, Nick Jackson +3
Background: Clinical guidelines are central to safe evidence-based medicine in modern healthcare, providing diagnostic criteria, treatment options and monitoring advice for a wide…
LLM Assistance for Pediatric Depression
Mariia Ignashina, Paulina Bondaronek, Dan Santel +2
Traditional depression screening methods, such as the PHQ-9, are particularly challenging for children in pediatric primary care due to practical limitations. AI has the potential…
A Data-Centric Approach to Detecting and Mitigating Demographic Bias in Pediatric Mental Health Text: A Case Study in Anxiety Detection
Julia Ive, Paulina Bondaronek, Vishal Yadav +10
Introduction: Healthcare AI models often inherit biases from their training data. While efforts have primarily targeted bias in structured data, mental health heavily depends on un…
Privacy-Preserving Behaviour of Chatbot Users: Steering Through Trust Dynamics
Julia Ive, Vishal Yadav, Mariia Ignashina +2
Introduction: The use of chatbots is becoming increasingly important across various aspects of daily life. However, the privacy concerns associated with these communications have n…
Safe Training with Sensitive In-domain Data: Leveraging Data Fragmentation To Mitigate Linkage Attacks
Mariia Ignashina, Julia Ive
Current text generation models are trained using real data which can potentially contain sensitive information, such as confidential patient information and the like. Under certain…
Source Code is a Graph, Not a Sequence: A Cross-Lingual Perspective on Code Clone Detection
Mohammed Ataaur Rahaman, Julia Ive
Source code clone detection is the task of finding code fragments that have the same or similar functionality, but may differ in syntax or structure. This task is important for sof…