Transforming Healthcare with AI – New Projects Fuelled by AI in Health Initiative
The AI in Health strategic initiative, led by the Monash Faculty of Information Technology, is designed to spark and support meaningful collaboration between technologists and health researchers across Monash – particularly those in the Faculty of Medicine, Nursing and Health Sciences. The goal: to co-create data-driven, AI-powered solutions that solve real clinical problems and improve healthcare.
By bringing together computer scientists, engineers, and clinicians, the initiative helps bridge the gap between cutting-edge AI research and the complex needs of frontline healthcare. Whether through diagnostic imaging, predictive modelling, or large language models, AI in Health ensures technical excellence is matched with real-world translational impact.
Bringing ideas to life, the AI in Health Collaborative Grant provides seed funding to cross-faculty teams to explore innovative and scalable clinically relevant AI solutions. Three projects from the Faculty of IT will be supported by this grant in 2025:
LEAPP-AI: AI Enhanced Workflows for Living Guidelines for Pregnancy and Postnatal care
Team: Dr Jackie Rong, with Dr Miranda Cumpston and Dr Shaira Bapitsa
This project is embedding AI into the development and upkeep of living guidelines for pregnancy and postnatal care—streamlining continuous updates and reducing manual workload while maintaining clinical accuracy.
AI-Enhanced Antimicrobial Stewardship: Automated Advisory System for Optimised Antimicrobial Management
Team: Dr Yasmeen George, Dr Ehsan Shareghi, Dr Iain Abbott, Professor Trisha Peel, Dr Nenad Macesic, Kelly Cairns and Rachael Leng
By applying NLP and machine learning to clinical data, this team is developing an AI-powered clinical advisory system that learns from existing AMS reviews to automate data extraction,synthesis, and preliminary assessment generation, enhancing efficiency while maintaining clinical oversight.
Development of a Digital Risk Stratification Tool for Seizure Recurrence in Emergency Care using LLMs
Team: Dr Deval Mehta and Dr Emma Foster
This project leverages large language models to predict seizure recurrence risk using ambulance and ED notes—enabling smarter triage decisions and reducing unnecessary hospital re-admissions.
These projects exemplify the transformative potential of interdisciplinary research at the intersection of AI and health. As the initiative continues to foster collaboration between the Faculty and health researchers, further engagement is invited to advance scalable, evidence-based AI solutions that address critical healthcare challenges and improve health system outcomes.