| How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals E Wu, K Wu, R Daneshjou, D Ouyang, DE Ho, J Zou Nature Medicine 27 (4), 582-584, 2021 | 307 | 2021 |
| Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach W Lotter, AR Diab, B Haslam, JG Kim, G Grisot, E Wu, K Wu, JO Onieva, ... Nature medicine 27 (2), 244-249, 2021 | 290 | 2021 |
| Conditional infilling GANs for data augmentation in mammogram classification E Wu, K Wu, D Cox, W Lotter Image Analysis for Moving Organ, Breast, and Thoracic Images: Third …, 2018 | 190* | 2018 |
| Learning scene gist with convolutional neural networks to improve object recognition K Wu, E Wu, G Kreiman 2018 52nd Annual Conference on Information Sciences and Systems (CISS), 1-6, 2018 | 28 | 2018 |
| Synthesizing lesions using contextual GANs improves breast cancer classification on mammograms E Wu, K Wu, W Lotter arXiv preprint arXiv:2006.00086, 2020 | 23 | 2020 |
| Analyses of canine cancer mutations and treatment outcomes using real-world clinico-genomics data of 2119 dogs K Wu, L Rodrigues, G Post, G Harvey, M White, A Miller, L Lambert, ... npj Precision Oncology 7 (8), 2023 | 14 | 2023 |
| Finding, monitoring, and checking claims computationally based on structured data B Walenz, Y Wu, S Song, E Sonmez, E Wu, K Wu, PK Agarwal, J Yang, ... Computation+ Journalism Symposium, 2014 | 13 | 2014 |
| Characterizing the clinical adoption of medical AI devices through US insurance claims K Wu, E Wu, B Theodorou, W Liang, C Mack, L Glass, J Sun, J Zou NEJM AI 1 (1), AIoa2300030, 2023 | 9 | 2023 |
| Validation of a deep learning mammography model in a population with low screening rates K Wu, E Wu, Y Wu, H Tan, G Sorensen, M Wang, B Lotter NeurIPS 2019 Fair ML for Health Workshop, 2019 | 9 | 2019 |
| Datainf: Efficiently estimating data influence in lora-tuned llms and diffusion models Y Kwon, E Wu, K Wu, J Zou arXiv preprint arXiv:2310.00902, 2023 | 8 | 2023 |
| Machine learning prediction of clinical trial operational efficiency K Wu, E Wu, M DAndrea, N Chitale, M Lim, M Dabrowski, K Kantor, ... The AAPS Journal 24 (3), 57, 2022 | 8 | 2022 |
| How well do LLMs cite relevant medical references? An evaluation framework and analyses K Wu, E Wu, A Cassasola, A Zhang, K Wei, T Nguyen, S Riantawan, ... arXiv preprint arXiv:2402.02008, 2024 | 6 | 2024 |
| Explaining medical AI performance disparities across sites with confounder Shapley value analysis E Wu, K Wu, J Zou Machine Learning for Health (ML4H), 2021 | 2 | 2021 |
| Collecting data when missingness is unknown: a method for improving model performance given under-reporting in patient populations K Wu, D Dahlem, C Hane, E Halperin, J Zou Conference on Health, Inference, and Learning (CHIL) 2023, 2023 | 1 | 2023 |
| Regulating AI Adaptation: An Analysis of AI Medical Device Updates K Wu, E Wu, K Rodolfa, D Ho, J Zou Conference on Health, Inference, and Learning (CHIL) 2024, 2024 | | 2024 |
| How faithful are RAG models? Quantifying the tug-of-war between RAG and LLMs' internal prior K Wu, E Wu, J Zou arXiv preprint arXiv:2404.10198, 2024 | | 2024 |
