Wu, Jia

AI in drug discovery and its clinical relevance

Qureshi, R., Irfan, M., Gondal, T. M., Khan, S., Wu, J., Hadi, M. U., Heymach, J., Le, X., Yan, H., & Alam, T. (2023) · Heliyon

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 319

Development and Validation of a Deep Learning CT Signature to Predict Survival and Chemotherapy Benefit in Gastric Cancer: A Multicenter, Retrospective Study

Jiang, Y., Jin, C., Yu, H., Wu, J., Chen, C., Yuan, Q., Huang, W., Hu, Y., Xu, Y., Zhou, Z., et al. (2021) · Annals of Surgery

Radiomics and Machine Learning in Medical ImagingGastric Cancer Management and Outcomes

Cited by 169

Noninvasive imaging evaluation of tumor immune microenvironment to predict outcomes in gastric cancer

Jiang, Y., Wang, H., Wu, J., Chen, C., Yuan, Q., Huang, W., Li, T., Xi, S., Hu, Y., Zhou, Z., et al. (2020) · Annals of Oncology

Radiomics and Machine Learning in Medical ImagingCancer Immunotherapy and Biomarkers

Cited by 169

Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study

Saad, M. B., Hong, L., Aminu, M., Vokes, N. I., Chen, P., Salehjahromi, M., Qin, K., Sujit, S. J., Lu, X., Young, E., et al. (2023) · The Lancet Digital Health

Radiomics and Machine Learning in Medical ImagingCancer Immunotherapy and Biomarkers

Cited by 121

Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence

Bai, X., Wang, H., Ma, L., Xu, Y., Gan, J., Fan, Z., Yang, F., Ma, K., Yang, J., Bai, S., et al. (2021) · Nature Machine Intelligence

COVID-19 diagnosis using AIPrivacy-Preserving Technologies in Data

Cited by 75

Magnetic resonance imaging and molecular features associated with tumor-infiltrating lymphocytes in breast cancer

Wu, J., Li, X., Teng, X., Rubin, D. L., Napel, S., Daniel, B. L., & Li, R. (2018) · Breast Cancer Research

MRI in cancer diagnosisRadiomics and Machine Learning in Medical Imaging

Cited by 53

Who is Responsible? The Data, Models, Users or Regulations? A Comprehensive Survey on Responsible Generative AI for a Sustainable Future

Raza, S., Qureshi, R., Zahid, A., Muneer, A., Zafar, A., Kamawal, S., Sadak, F., Fioresi, J., Saeed, M., Sapkota, R., et al. (2025) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 5