The University of Adelaide
Relevante Arbeiten
Meistzitierte Publikationen im Bereich Gesundheit & MedTech
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
Gary S. Collins, Karel G.M. Moons, Paula Dhiman et al.
2024 · 1.417 Zit.
The false hope of current approaches to explainable artificial intelligence in health care
Marzyeh Ghassemi, Luke Oakden‐Rayner, Andrew L. Beam
2021 · 1.195 Zit.
AI recognition of patient race in medical imaging: a modelling study
Judy Wawira Gichoya, Imon Banerjee, Ananth Reddy Bhimireddy et al.
2022 · 465 Zit.
AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system
Bo Wang, Shuo Jin, Qingsen Yan et al.
2020 · 365 Zit.
The ethical, legal and social implications of using artificial intelligence systems in breast cancer care
Stacy M. Carter, Wendy Rogers, Khin Than Win et al.
2019 · 285 Zit.
A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology
Jane Scheetz, Philip Rothschild, Myra B. McGuinness et al.
2021 · 259 Zit.
The value of standards for health datasets in artificial intelligence-based applications
Anmol Arora, Joseph Alderman, Joanne Palmer et al.
2023 · 235 Zit.
The impact of generative AI on higher education learning and teaching: A study of educators’ perspectives
Daniel Lee, Matthew Arnold, Amit Srivastava et al.
2024 · 225 Zit.
Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist
Partho P. Sengupta, Sirish Shrestha, Béatrice Berthon et al.
2020 · 205 Zit.
Deep learning-based cardiovascular image diagnosis: A promising challenge
Kelvin K. L. Wong, Giancarlo Fortino, Derek Abbott
2019 · 199 Zit.
The medical algorithmic audit
Xiaoxuan Liu, Ben Glocker, Melissa D. McCradden et al.
2022 · 199 Zit.
PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods
Karel G.M. Moons, Johanna AAG Damen, T. K. Kaul et al.
2025 · 171 Zit.
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner et al.
2024 · 154 Zit.
Recommendations and future directions for supervised machine learning in psychiatry
Micah Cearns, Tim Hahn, Bernhard T. Baune
2019 · 124 Zit.
Deep Learning in the Prediction of Ischaemic Stroke Thrombolysis Functional Outcomes
Stephen Bacchi, Toby Zerner, Luke Oakden‐Rayner et al.
2019 · 105 Zit.