AIMC Topic: Australia
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Development of Machine Learning-Based Risk Prediction Models to Predict Rapid Weight Gain in Infants: Analysis of Seven Cohorts.
JMIR public health and surveillance
Jun 18, 2025
BACKGROUND: Rapid weight gain (RWG) during infancy, defined as an upward crossing of one centile line on a weight growth chart, is highly predictive of subsequent obesity risk. Identification of infant RWG could facilitate obesity risk assessment fro...
A thematic analysis of what Australians state would change their minds on climate change.
Scientific reports
Apr 22, 2025
What do Australians believe would change their current opinions about climate change? In this study, we used audience segmentation analysis through the Six Americas Short Survey to identify groups of climate opinion holders within a representative sa...
Identifying and addressing challenges in gross pollutant trap maintenance: perspectives from the Australian stormwater industry.
Marine pollution bulletin
Apr 16, 2025
A common approach to removing pollution from stormwater is through the installation of gross pollutant traps (GPTs). However, GPTs are often not maintained effectively, leading to pollution accumulation and additional pollution bypassing into natural...
The use of AI large language models by university students for assignment preparation.
Advances in physiology education
Mar 25, 2025
Using an opportunity where students were explicitly permitted to use artificial intelligence (AI) applications to prepare an assignment, we compared the practices and beliefs of two distinct student cohorts: second-year science students at a large me...
Exploring the Views of Dermatologists, General Practitioners, and Melanographers on the Use of AI Tools in the Context of Good Decision-Making When Detecting Melanoma: Qualitative Interview Study.
JMIR dermatology
Mar 24, 2025
BACKGROUND: Evidence that artificial intelligence (AI) may improve melanoma detection has led to calls for increased human-AI collaboration in clinical workflows. However, AI-based support may entail a wide range of specific functions for AI. To appr...
Attributional patterns toward students with and without learning disabilities: Artificial intelligence models vs. trainee teachers.
Research in developmental disabilities
Mar 15, 2025
This study explored differences in the attributional patterns of four advanced artificial intelligence (AI) Large Language Models (LLMs): ChatGPT3.5, ChatGPT4, Claude, and Gemini) by focusing on feedback, frustration, sympathy, and expectations of fu...
Evaluating the role of AI chatbots in patient education for abdominal aortic aneurysms: a comparison of ChatGPT and conventional resources.
ANZ journal of surgery
Mar 5, 2025
BACKGROUNDS: Abdominal aortic aneurysms (AAA) carry significant risks, yet patient understanding is often limited, with online resources typically low quality. ChatGPT, an artificial intelligence (AI) chatbot, presents a new frontier in patient educa...
Transforming residential water end use analysis: unleashing insights from widespread low-resolution smart metering data.
Water research
Feb 20, 2025
Transforming smart meter data captured at low-resolution litre intervals of 15 to 60 mins into residential water end use data provides valuable insights for water businesses and their customers. Water end use data is crucial for developing effective ...
A multicentre implementation trial of an Artificial Intelligence-driven biomarker to inform Shared decisions for androgen deprivation therapy in men undergoing prostate radiotherapy: the ASTuTE protocol.
BMC cancer
Feb 13, 2025
BACKGROUND: Androgen deprivation therapy (ADT) improves outcomes in men undergoing definitive radiotherapy for prostate cancer but carries significant toxicities. Clinical parameters alone are insufficient to accurately identify patients who will der...
Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and Australia.
Translational psychiatry
Jan 21, 2025
Existing dementia prediction models using non-neuroimaging clinical measures have been limited in their ability to identify disease. This study used machine learning to re-examine the diagnostic potential of clinical measures for dementia. Data was s...
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