Stanford University
Relevante Arbeiten
Meistzitierte Publikationen im Bereich Gesundheit & MedTech
A guide to deep learning in healthcare
Andre Esteva, Alexandre Robicquet, Bharath Ramsundar et al.
2018 · 4.222 Zit.
Scalable and accurate deep learning with electronic health records
Alvin Rajkomar, Eyal Oren, Kai Chen et al.
2018 · 2.249 Zit.
AI in health and medicine
Pranav Rajpurkar, Emma Chen, Oishi Banerjee et al.
2022 · 2.234 Zit.
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.
Foundation models for generalist medical artificial intelligence
Michael Moor, Oishi Banerjee, Zahra Shakeri Hossein Abad et al.
2023 · 1.368 Zit.
Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data
Milena Gianfrancesco, Suzanne Tamang, Jinoos Yazdany et al.
2018 · 1.265 Zit.
Artificial Intelligence in Cardiology
Kipp W. Johnson, Jessica Torres Soto, Benjamin S. Glicksberg et al.
2018 · 1.105 Zit.
The Medical Segmentation Decathlon
Michela Antonelli, Annika Reinke, Spyridon Bakas et al.
2022 · 1.101 Zit.
Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies
Myura Nagendran, Yang Chen, Christopher A. Lovejoy et al.
2020 · 987 Zit.
Preparing Medical Imaging Data for Machine Learning
Martin J. Willemink, Wojciech A. Koszek, Cailin Hardell et al.
2020 · 917 Zit.
Do no harm: a roadmap for responsible machine learning for health care
Jenna Wiens, Suchi Saria, Mark Sendak et al.
2019 · 893 Zit.
Swarm Learning for decentralized and confidential clinical machine learning
Stefanie Warnat‐Herresthal, Hartmut Schultze, Krishnaprasad Lingadahalli Shastry et al.
2021 · 795 Zit.
AI can be sexist and racist — it’s time to make it fair
James Zou, Londa Schiebinger
2018 · 727 Zit.
Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines
Shih-Cheng Huang, Anuj Pareek, Saeed Seyyedi et al.
2020 · 726 Zit.
Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet
Nicholas Bien, Pranav Rajpurkar, Robyn L. Ball et al.
2018 · 705 Zit.