University of Chicago

Scalable and accurate deep learning with electronic health records

Alvin Rajkomar, Eyal Oren, Kai Chen et al.

2018 · 2.337 Zit.

Artificial intelligence in cancer imaging: Clinical challenges and applications

Wenya Linda Bi, Ahmed Hosny, Matthew B. Schabath et al.

2019 · 1.804 Zit.

Ensuring Fairness in Machine Learning to Advance Health Equity

Alvin Rajkomar, Michaela Hardt, Michael Howell et al.

2018 · 1.121 Zit.

Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards

Matthew M. Churpek, Trevor C. Yuen, Christopher Winslow et al.

2016 · 646 Zit.

Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers

Catherine A. Gao, Frederick M. Howard, Nikolay S. Markov et al.

2023 · 636 Zit.

Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness

Sebastian J. Vollmer, Bilal A. Mateen, Gergő Bohner et al.

2020 · 465 Zit.

Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers

Catherine A. Gao, Frederick M. Howard, Nikolay S. Markov et al.

2022 · 410 Zit.

Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use

Akhil Narang, Richard Bae, Ha Hong et al.

2021 · 401 Zit.

Big Data and Data Science in Critical Care

L. Nelson Sanchez‐Pinto, Yuan Luo, Matthew M. Churpek

2018 · 289 Zit.

Explaining Decision-Making Algorithms through UI

Hao-Fei Cheng, Ruotong Wang, Zheng Zhang et al.

2019 · 285 Zit.

Comparison of variable selection methods for clinical predictive modeling

L. Nelson Sanchez‐Pinto, Laura Ruth Venable, John Fahrenbach et al.

2018 · 275 Zit.

Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care

Marshall H. Chin, Nasim Afsarmanesh, Arlene S. Bierman et al.

2023 · 210 Zit.

Noise injection for training artificial neural networks: A comparison with weight decay and early stopping

Richard M. Zur, Yulei Jiang, Lorenzo L. Pesce et al.

2009 · 203 Zit.

Automating Ischemic Stroke Subtype Classification Using Machine Learning and Natural Language Processing

Ravi Garg, Elissa H. Oh, Andrew M. Naidech et al.

2019 · 152 Zit.

Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment

Karen Drukker, Weijie Chen, Judy Wawira Gichoya et al.

2023 · 124 Zit.