Oermann, Eric Karl

Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study

Zech, J. R., Badgeley, M. A., Liu, M., Costa, A. B., Titano, J. J., & Oermann, E. K. (2018) · PLOS Medicine

COVID-19 diagnosis using AIRadiomics and Machine Learning in Medical Imaging

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Federated learning for predicting clinical outcomes in patients with COVID-19

Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Wood, B. J., Tsai, C.-S., et al. (2021) · Nature Medicine

Machine Learning in HealthcareCOVID-19 diagnosis using AI

Cited by 842

Health system-scale language models are all-purpose prediction engines

Jiang, L. Y., Liu, X. C., Nejatian, N. P., Nasir-Moin, M., Wang, D., Abidin, A., Eaton, K., Riina, H. A., Laufer, I., Punjabi, P., et al. (2023) · Nature

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 460

Automated deep-neural-network surveillance of cranial images for acute neurologic events

Titano, J. J., Badgeley, M., Schefflein, J., Pain, M., Su, A., Cai, M., Swinburne, N., Zech, J., Kim, J., Bederson, J., et al. (2018) · Nature Medicine

Artificial Intelligence in Healthcare and EducationAcute Ischemic Stroke Management

Cited by 458

Medical large language models are vulnerable to data-poisoning attacks

Alber, D. A., Yang, Z., Alyakin, A., Yang, E., Rai, S., Valliani, A. A., Zhang, J., Rosenbaum, G. R., Amend-Thomas, A. K., Kurland, D. B., et al. (2025) · Nature Medicine

Adversarial Robustness in Machine LearningMisinformation and Its Impacts

Cited by 242

Natural language-based machine learning models for the annotation of clinical radiology reports

Zech, J., Pain, M., Titano, J., Badgeley, M., Schefflein, J., Su, A., Costa, A., Bederson, J., Lehar, J., & Oermann, E. K. (2018) · Radiology

Machine Learning in HealthcareBiomedical Text Mining and Ontologies

Cited by 142

General-purpose large language models outperform specialized clinical AI tools on medical benchmarks

Vishwanath, K., Alyakin, A., Ghosh, M., Hage, A., Neifert, S. N., Orillac, C., Mandelberg, N. J., Khan, H. A., Lee, J. V., Yao, J. J., et al. (2026) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 49

Confounding variables can degrade generalization performance of radiological deep learning models

Zech, J. R., Badgeley, M. A., Liu, M., Costa, A. B., Titano, J. J., & Oermann, E. K. (2018) · arXiv

COVID-19 diagnosis using AIArtificial Intelligence in Healthcare and Education

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MetaGP: A generative foundation model integrating electronic health records and multimodal imaging for addressing unmet clinical needs

Liu, F., Zhou, H., Wang, K., Yu, Y., Gao, Y., Sun, Z., Liu, S., Sun, S., Zou, Z., Li, Z., et al. (2025) · Cell Reports Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

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Robust Prediction of Non-home Discharge After Thoracolumbar Spine Surgery With Ensemble Machine Learning and Validation on a Nationwide Cohort

Valliani, A. A., Kim, N. C., Martini, M. L., Gal, J. S., Neifert, S. N., Feng, R., Geng, E. A., Kim, J. S., Cho, S. K., Oermann, E. K., et al. (2022) · World Neurosurgery

Machine Learning in HealthcareArtificial Intelligence in Healthcare

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