COVID-19 Image Data Collection: Prospective Predictions Are the Future JP Cohen, P Morrison, L Dao, K Roth, TQ Duong, M Ghassemi arXiv preprint arXiv:2006.11988, 2020 | 867 | 2020 |
Do no harm: a roadmap for responsible machine learning for health care J Wiens, S Saria, M Sendak, M Ghassemi, VX Liu, F Doshi-Velez, K Jung, ... Nature medicine 25 (9), 1337-1340, 2019 | 707 | 2019 |
The false hope of current approaches to explainable artificial intelligence in health care M Ghassemi, L Oakden-Rayner, AL Beam The Lancet Digital Health 3 (11), e745-e750, 2021 | 678 | 2021 |
Ethical machine learning in healthcare IY Chen, E Pierson, S Rose, S Joshi, K Ferryman, M Ghassemi Annual Review of Biomedical Data Science 4, 123-144, 2021 | 397 | 2021 |
A Review of Challenges and Opportunities in Machine Learning for Health M Ghassemi, T Naumann, P Schulam, AL Beam, IY Chen, R Ranganath AMIA Summits on Translational Science Proceedings 191, 2020 | 392* | 2020 |
Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations L Seyyed-Kalantari, H Zhang, M McDermott, IY Chen, M Ghassemi Nature medicine 27 (12), 2176-2182, 2021 | 371 | 2021 |
Can AI Help Reduce Disparities in General Medical and Mental Health Care? IY Chen, P Szolovits, M Ghassemi AMA Journal of Ethics 21 (2), 167-179, 2019 | 304 | 2019 |
Challenges to the reproducibility of machine learning models in health care AL Beam, AK Manrai, M Ghassemi Jama 323 (4), 305-306, 2020 | 282 | 2020 |
Unfolding Physiological State: Mortality Modelling in Intensive Care Units M Ghassemi, T Naumann, F Doshi-Velez, N Brimmer, R Joshi, ... KDD 2014, 2014 | 279 | 2014 |
AI recognition of patient race in medical imaging: a modelling study JW Gichoya, I Banerjee, AR Bhimireddy, JL Burns, LA Celi, LC Chen, ... The Lancet Digital Health 4 (6), e406-e414, 2022 | 277 | 2022 |
A multivariate timeseries modeling approach to severity of illness assessment and forecasting in icu with sparse, heterogeneous clinical data M Ghassemi, MAF Pimentel, T Naumann, T Brennan, DA Clifton, ... Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015 | 274 | 2015 |
CheXclusion: Fairness gaps in deep chest X-ray classifiers L Seyyed-Kalantari, G Liu, M McDermott, IY Chen, M Ghassemi BIOCOMPUTING 2021: Proceedings of the Pacific Symposium, 232-243, 2020 | 266 | 2020 |
Do as AI say: susceptibility in deployment of clinical decision-aids S Gaube, H Suresh, M Raue, A Merritt, SJ Berkowitz, E Lermer, ... NPJ digital medicine 4 (1), 31, 2021 | 265 | 2021 |
Predicting covid-19 pneumonia severity on chest x-ray with deep learning JP Cohen, L Dao, K Roth, P Morrison, Y Bengio, AF Abbasi, B Shen, ... Cureus 12 (7), 2020 | 264 | 2020 |
Clinically accurate chest x-ray report generation G Liu, TMH Hsu, M McDermott, W Boag, WH Weng, P Szolovits, ... Machine Learning for Healthcare Conference, 249-269, 2019 | 262 | 2019 |
Clinical Intervention Prediction and Understanding with Deep Neural Networks H Suresh, N Hunt, A Johnson, LA Celi, P Szolovits, M Ghassemi Machine Learning for Healthcare Conference, 322-337, 2017 | 259* | 2017 |
Reproducibility in machine learning for health research: Still a ways to go MBA McDermott, S Wang, N Marinsek, R Ranganath, L Foschini, ... Science Translational Medicine 13 (586), eabb1655, 2021 | 224* | 2021 |
Continuous state-space models for optimal sepsis treatment: a deep reinforcement learning approach A Raghu, M Komorowski, LA Celi, P Szolovits, M Ghassemi Machine Learning for Healthcare Conference, 147-163, 2017 | 220 | 2017 |
Mimic-extract: A data extraction, preprocessing, and representation pipeline for mimic-iii S Wang, MBA McDermott, G Chauhan, M Ghassemi, MC Hughes, ... Proceedings of the ACM Conference on Health, Inference, and Learning, 222-235, 2020 | 201 | 2020 |
Deep Reinforcement Learning for Sepsis Treatment A Raghu, M Komorowski, I Ahmed, L Celi, P Szolovits, M Ghassemi arXiv preprint arXiv:1711.09602, 2017 | 200 | 2017 |