Area of research
Health Informatics · Radiology, Nuclear Medicine and Imaging
Research interest
Research interests include Artificial Intelligence in Healthcare and Education, Radiology practices and education, Radiomics and Machine Learning in Medical Imaging, and Medical Imaging and Analysis.
PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods
CANAIRI: the Collaboration for Translational Artificial Intelligence Trials in healthcare
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods: a Korean translation
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations
Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA
Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA
Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA
Medical artificial intelligence for clinicians: the lost cognitive perspective
Developing, purchasing, implementing and monitoring AI tools in radiology: Practical considerations. A multi‐society statement from the ACR, CAR, ESR, RANZCR & RSNA
Tackling Algorithmic Bias and Promoting Transparency in Health Datasets: The STANDING Together Consensus Recommendations
The value of standards for health datasets in artificial intelligence-based applications
Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy
Taking Off with AI: Lessons from Aviation for Healthcare
AI recognition of patient race in medical imaging: a modelling study
The medical algorithmic audit
Tackling bias in AI health datasets through the STANDING Together initiative