Ballarat Health Services

Machine learning approaches for risk prediction after percutaneous coronary intervention: a systematic review and meta-analysis

Ammar Zaka, D. Mutahar, J. Gorcilov et al.

2024 · 18 Zit.

Artificial intelligence for surgical services in Australia and New Zealand: opportunities, challenges and recommendations

Joshua G. Kovoor, Stephen Bacchi, Prakriti Sharma et al.

2024 · 12 Zit.

Generative AI’s healthcare professional role creep: a cross-sectional evaluation of publicly accessible, customised health-related GPTs

Bianca Chu, Natansh D. Modi, Bradley D. Menz et al.

2025 · 8 Zit.

When One Size Does not Fit All—Artificial Intelligence in Australian Rural Health

Lewis Hains, J Kovoor, Brandon Stretton et al.

2025 · 7 Zit.

Quality of Information on Wilms Tumor From Artificial Intelligence Chatbots: What Are Your Patients and Their Families Reading?

Peter Stapleton, Jordan Santucci, Thomas P. Cundy et al.

2025 · 7 Zit.

Large language model‐supported interactive case‐based learning: a pilot study

Haelynn Gim, Benjamin K. Cook, Jasmin Le et al.

2025 · 4 Zit.

Quality of information on hypospadias from artificial intelligence chatbots: How safe is AI for patient and family information?

Peter Stapleton, Jordan Santucci, Monica Thet et al.

2025 · 3 Zit.

Epilepsy surgery candidate identification with artificial intelligence: An implementation study

Sheryn Tan, Rudy Goh, Alexander C. Wright et al.

2025 · 3 Zit.

The Adelaide Score: prospective implementation of an artificial intelligence system to improve hospital and cost efficiency

Joshua G. Kovoor, Brandon Stretton, Aashray Gupta et al.

2025 · 3 Zit.

The future is bright: artificial intelligence for trainee medical officers in <scp>Australia</scp> and <scp>New Zealand</scp>

Joshua G. Kovoor, Harry Smallbone, Alexander Jenkins et al.

2024 · 2 Zit.

Better off alone? Artificial intelligence can demonstrate superior performance without clinician input

Joshua G. Kovoor, Daksh Tyagi, Ashley M. Hopkins et al.

2025 · 1 Zit.

Comparison of machine learning and traditional methods for prediction of adverse clinical events after percutaneous coronary intervention

A. Zaka, J. Gorcilov, D. Mutahar et al.

2024 · 1 Zit.

Automating cancer registries: Pearls and pitfalls

Shrirajh Satheakeerthy, Mark Beecher, Andrew Booth et al.

2025 · 1 Zit.

Enhancing Diagnostic and Postoperative Outcome Predictions Through Machine Learning: A Focused Analysis on Noncardiac and Cardiac Surgeries

A Lombardo, Christopher Hannemann, Saboor Aftab et al.

2025 · 1 Zit.

Comparison of Machine Learning and Traditional Methods for Prediction of Adverse Clinical Events After Percutaneous Coronary Intervention

A. Zaka, J. Gorcilov, D. Mutahar et al.

2024 · 0 Zit.