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Transforming radiology in New Zealand - From accuracy to implementation of AI

Year:
2025
Duration:
24 months
Approved budget:
$697,273.00
Researchers:
Professor Robyn Whittaker
,
Dr Daniel Stahlhoven
,
Dr Cheng Kai Jin
,
Associate Professor Rosie Dobson
,
Dr Mark Vanderklei
,
Professor Gillian Dobbie
,
Mrs Musheerah Nuha
,
Dr Zhenqiang Wu
,
Dr Helen Wihongi
Host:
Health New Zealand - Capital, Coast and Hutt Valley
Health issue:
Respiratory/asthma
Proposal type:
AI in Healthcare Project
Lay summary
This research will evaluate whether artificial intelligence (AI) can help support faster and more accurate interpretation and reporting on chest Xrays (CXR) in our hospitals. Delays in reading and reporting on radiology images such as Xrays can lead to delays in commencing treatment and impact patient flow through hospitals. Our project has three phases where we will (1) compare the accuracy of CXR reporting in periods with and without AI assistance, (2) compare reporting times, outsourcing costs and workflow impacts in periods with and without AI assistance in a hospital radiology department, and (3) interview clinicians and health service leaders about the barriers and enablers to the adoption of AI tools within these radiology departments. This will inform the development of Health NZ plans for implementing AI within radiology services safely and effectively across the country.