Zhang, Haoran

Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations

Seyyed-Kalantari, L., Zhang, H., McDermott, M. B. A., Chen, I. Y., & Ghassemi, M. (2021) · Nature Medicine

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 928

AI recognition of patient race in medical imaging: a modelling study

Gichoya, J. W., Banerjee, I., Bhimireddy, A. R., Burns, J. L., Celi, L. A., Chen, L.-C., Correa, R., Dullerud, N., Ghassemi, M., Huang, S.-C., et al. (2022) · The Lancet Digital Health

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 611

The limits of fair medical imaging AI in real-world generalization

Yang, Y., Zhang, H., Gichoya, J. W., Katabi, D., & Ghassemi, M. (2024) · Nature Medicine

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 285

Hurtful words: quantifying biases in clinical contextual word embeddings

Zhang, H., Lu, A. X., Abdalla, M., McDermott, M., & Ghassemi, M. (2020) · Proceedings of the ACM Conference on Health, Inference, and Learning

Machine Learning in HealthcareEthics and Social Impacts of AI

Cited by 122

A Closer Look at AUROC and AUPRC under Class Imbalance

McDermott, M. B. A., Zhang, H., Hansen, L. H., Angelotti, G., & Gallifant, J. (2024) · arXiv

Imbalanced Data Classification TechniquesMachine Learning and Data Classification

Cited by 65

Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings

Zhang, H., Lu, A. X., Abdalla, M., McDermott, M., & Ghassemi, M. (2020) · arXiv

Machine Learning in HealthcareEthics and Social Impacts of AI

Cited by 37