周洪宇博士的研究聚焦于构建可扩展、可信、具有临床实用价值的医学人工智能系统，致力于通过智能技术推动高质量医疗资源的普惠化。以“数据”和“知识”为支点，系统开展了以下研究方向：

1.智能健康生态系统：研发以病人为中心的医学人工智能系统，致力于构建病人可信赖、可理解且可使用的智能健康技术。通过推动AI辅助的健康管理、个性化干预与患者赋能，使患者成为健康管理的核心主体，推动医学人工智能向更人性化、普惠化和可信赖方向发展。

2.医疗决策通才模型：推动通用医学人工智能系统的构建，研发兼具感知、推理与生成能力的多模态大模型，使其能够胜任跨专科、多场景的临床任务，助力优质医疗资源的广泛可及。

3.医学影像基础模型：致力于构建新一代自监督医学视觉表征学习框架，探索弱监督与无监督范式下的模型泛化机制，以降低对昂贵标注数据的依赖，提升模型在跨机构、跨设备环境中的稳健性与可迁移性。

1.Zhou, H. Y., Yu, Y., Wang, C., Zhang, S., Gao, Y., Pan, J., Shao, J., Lu, G., Zhang, K., & Li, W. (2023). A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics. Nature Biomedical Engineering, 7, 743-755.

2.Zhou, H. Y., Chen, X., Zhang, Y., Luo, R., Wang, L., & Yu, Y.Y. (2021). Generalized radiograph representation learning via cross-supervision between images and free-text radiology reports. Nature Machine Intelligence, 4, 32 - 40.

3.Zhou, H. Y., Lu, C., Chen, C., Yang, S., & Yu, Y. (2023). A Unified Visual Information Preservation Framework for Self-supervised Pre-Training in Medical Image Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 8020-8035.

4.Zhou, H. Y., Lian, C., Wang, L., & Yu, Y. Advancing Radiograph Representation Learning with Masked Record Modeling. In The Eleventh International Conference on Learning Representations.

5.Zhou, H. Y., Fu, Y., Zhang, Z., Cheng, B., & Yu, Y. (2023). Protein representation learning via knowledge enhanced primary structure reasoning. In The Eleventh international conference on learning representations.

6.Zhou, H. Y., Lu, C., Yang, S., Han, X., & Yu, Y. (2021). Preservational learning improves self-supervised medical image models by reconstructing diverse contexts. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 3499-3509).

7.Johri, S., Jeong, J., Tran, B.A., Schlessinger, D.I., Wongvibulsin, S., Barnes, L., Zhou, H. Y., Cai, Z.R., Van Allen, E.M., Kim, D.A., Daneshjou, R., & Rajpurkar, P. (2025). An evaluation framework for clinical use of large language models in patient interaction tasks. Nature Medicine, 31(1), 77-86.

8.Wang, J., Wang, K., Yu, Y., Lu, Y., Xiao, W., Sun, Z., Liu, F., Zou, Z., Gao, Y., Yang, L., Zhou, H. Y., Miao, H., Zhao, W., Huang, L., Zeng, L., Guo, R., Chong, I., Deng, B., Cheng, L., Chen, X., Luo, J., Zhu, M., Baptista‐Hon, D.T., Monteiro, O., Li, M., Ke, Y., Li, J., Zeng, S., Guan, T., Zeng, J., Xue, K., Oermann, E.K., Luo, H., Yin, Y., Zhang, K., & Qu, J. (2024). Self-improving generative foundation model for synthetic medical image generation and clinical applications. Nature Medicine, 31(2), 609-617.

9.Huang, W., Li, C., Zhou, H. Y., Yang, H., Liu, J., Liang, Y., ... & Wang, S. (2024). Enhancing representation in radiography-reports foundation model: A granular alignment algorithm using masked contrastive learning. Nature Communications, 15(1), 7620.

10.Gao, Y., Ventura-Diaz, S., Wang, X., He, M., Xu, Z., Weir, A., Zhou, H. Y., Zhang, T., Van Duijnhoven, F., Han, L., Li, X., D'Angelo, A., Longo, V., Liu, Z., Teuwen, J., Kok, M., Beets-Tan, R., Horlings, H.M., Tan, T., & Mann, R. (2024). An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer. Nature Communications, 15(1), 9613.

1.2024年“斯坦福大学/爱思唯尔全球前2%顶尖科学家”

2.香港大学基金会“杰出博士奖”

3.香港大学基金会“杰出研究成果奖”

4.国际华人医学影像协会（ICMA）博士研究生奖学金

5.“精确自动化脊柱曲率评估挑战赛”冠军（参赛队伍79支，排名第1）

6.“胸部危险器官（OAR）分割挑战赛”冠军（参赛队伍638支，排名第1）

7.“AI4Health全球挑战赛”冠军（参赛队伍200支，排名第1）