Australian Centre for Robotic Vision
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 · 1.737 Zit.
The false hope of current approaches to explainable artificial intelligence in health care
Marzyeh Ghassemi, Luke Oakden‐Rayner, Andrew L. Beam
2021 · 1.273 Zit.
Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension
Xiaoxuan Liu, Samantha Cruz Rivera, David Moher et al.
2020 · 951 Zit.
Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension
Samantha Cruz Rivera, Xiaoxuan Liu, An‐Wen Chan et al.
2020 · 565 Zit.
AI recognition of patient race in medical imaging: a modelling study
Judy Wawira Gichoya, Imon Banerjee, Ananth Reddy Bhimireddy et al.
2022 · 488 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 · 267 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 · 261 Zit.
The value of standards for health datasets in artificial intelligence-based applications
Anmol Arora, Joseph Alderman, Joanne Palmer et al.
2023 · 250 Zit.
Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study
Jarrel Seah, Cyril Tang, Quinlan D. Buchlak et al.
2021 · 219 Zit.
The medical algorithmic audit
Xiaoxuan Liu, Ben Glocker, Melissa D. McCradden et al.
2022 · 213 Zit.
Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist
Partho P. Sengupta, Sirish Shrestha, Béatrice Berthon et al.
2020 · 210 Zit.
Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis
Sergei Bedrikovetski, Nagendra N. Dudi‐Venkata, Hidde M. Kroon et al.
2021 · 158 Zit.
Deep Learning in the Prediction of Ischaemic Stroke Thrombolysis Functional Outcomes
Stephen Bacchi, Toby Zerner, Luke Oakden‐Rayner et al.
2019 · 106 Zit.
Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations
Joseph Alderman, Joanne Palmer, Elinor Laws et al.
2024 · 101 Zit.
Validation and algorithmic audit of a deep learning system for the detection of proximal femoral fractures in patients in the emergency department: a diagnostic accuracy study
Lauren Oakden‐Rayner, William A. Gale, Thomas A. Bonham et al.
2022 · 87 Zit.