Research Area
Clinical Decision Support
Developing AI-assisted tools that complement human expertise in clinical settings, with rigorous evaluation of real-world impact.
Overview
As AI systems are increasingly deployed in clinical settings, rigorous evaluation methods are essential to ensure these tools genuinely improve patient outcomes. Our research focuses on developing and testing AI-assisted clinical decision support systems that complement—rather than replace—human expertise.
Key areas of investigation include:
- Clinician-in-the-loop AI: Understanding how clinicians interact with AI recommendations and designing interfaces that support appropriate trust calibration
- Soft ground truth methods: Developing statistical approaches for evaluating AI when gold-standard labels are uncertain
- Implementation science: Studying how workflow integration, training, and population factors affect AI effectiveness
- Equity evaluation: Ensuring clinical AI performs equitably across demographic groups
Active Projects
Evaluating AI-Assisted Clinical Decision Support
Rigorous experimental methods for understanding how AI recommendations affect clinician decision-making, accuracy, and behavior in clinical risk assessment.
AI for Primary Care Diagnostics
Developing and evaluating generative AI systems to improve diagnostic accuracy in resource-limited primary care settings, using reinforcement learning with expert feedback.
AIM-AHEAD
Advancing clinical decision support that complements human expertise through the NIH AIM-AHEAD consortium
Clinical AI Evaluation Methods
Developing rigorous methodologies to assess AI-assisted clinical decision support systems, from algorithm performance to real-world clinical impact.
Related Publications
View all publicationsEpidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: a scoping review
Adhikari, S. P., Meng, S., Wu, Yuju, Mao, Yu-Ping, Ye, R., Wang, Qingzhi, Sun, Chang, Sylvia, Sean Yuji, Rozelle, S., Raat, H., Zhou, Huan (2020)
Infectious Diseases of Poverty
DOI: 10.1186/s40249-020-00646-xViolence against health care workers in China, 2013–2016: evidence from the national judgment documents
Cai, Ruilie, Tang, Ji, Deng, Chenhui, Lv, Guofan, Xu, Xiaohe, Sylvia, Sean Yuji, Pan, Jay (2019)
Human Resources for Health
DOI: 10.1186/s12960-019-0440-yExplaining the declining utilization of village clinics in rural China over time: A decomposition approach.
Chen, Yunwei, Sylvia, Sean Yuji, Wu, P., Yi, Hongmei (2022)
Social Science & Medicine (1967)
DOI: 10.1016/j.socscimed.2022.114978Structural Determinants of Child Health in Rural China: The Challenge of Creating Health Equity
Chen, Yunwei, Sylvia, Sean Yuji, Dill, Sarah-Eve, Rozelle, S. (2022)
International Journal of Environmental Research and Public Health
DOI: 10.3390/ijerph192113845Effect of an mHealth-Supported Healthy Future Programme to Improve Type 2 Diabetes Management in Nairobi, Kenya: A Cluster Randomised Controlled Trial
Chen, Huanhuan, Ndegwa, Stephen, Kwaro, Daniel, Otieno, Walter, Oyugi, Elizabeth, Sylvia, Sean (2023)
The Lancet Global Health
Evaluation of a village-based digital health kiosks program: A protocol for a cluster randomized clinical trial
Cheng, Weibin, Zhang, Z., Hoelzer, Samantha, Tang, Weiming, Liang, Yizhi, Du, Yumeng, Xue, Hao, Zhou, Qiru, Yip, W., Ma, Xiaochen, Tian, Junzhang, Sylvia, Sean Yuji (2022)
Digital Health
DOI: 10.1177/20552076221129100Global health development assistance remained steady in 2013 but did not align with recipients' disease burden.
Dieleman, J., Graves, Casey M, Templin, Tara, Johnson, Elizabeth K, Baral, R., Leach-Kemon, Katherine, Haakenstad, Annie, Murray, C. (2014)
Health Affairs
DOI: 10.1377/hlthaff.2013.1432Spending on health and HIV/AIDS: domestic health spending and development assistance in 188 countries, 1995–2015
Dieleman, J., Haakenstad, Annie, Micah, A., Moses, Mark W, Abbafati, C., Acharya, P., Adhikari, Tara Ballav, Adou, A. K., Kiadaliri, Aliasghar Ahmad, Alam, K., Alizadeh-Navaei, Reza, Alkerwi, A., Ammar, W., Antonio, C., Aremu, O., Asgedom, S. W., Atey, T., Ávila-Burgos, L., Awasthi, A., Ayer, R., Badali, H., Banach, M., Banstola, A., Barać, A., Belachew, A., Birungi, C., Bragazzi, N., Breitborde, N., Cahuana-Hurtado, Lucero, Car, J., Catalá-López, F., Chapin, Abigail, Dandona, L., Dandona, R., Daryani, A., Dharmaratne, S., Dubey, M., Edessa, Dumessa, Eldrenkamp, Erika, Eshrati, B., Faro, Andre, Feigl, A., Fenny, A., Fischer, F., Foigt, N., Foreman, Kyle, Fullman, N., Ghimire, M., Goli, Srinivas, Hailu, A., Hamidi, S., Harb, H., Hay, Simon Iain, Hendrie, D., Ikilezi, G., Javanbakht, Mehdi, John, D., Jonas, J., Kaldjian, A., Kasaeian, A., Kates, J., Khalil, I., Khang, Y., Khubchandani, J., Kim, Y., Kinge, J., Kosen, S., Krohn, Kristopher J., Kumar, G., Lam, H., Listl, S., Razek, H. Magdy Abd El, Razek, M. Magdy Abd El, Majeed, A., Malekzadeh, R., Malta, D., Mensah, G., Meretoja, A., Miller, T., Mirrakhimov, E., Mlashu, Fitsum Weldegebreal, Mohammed, Ebrahim, Mohammed, S., Naghavi, M., Nangia, V., Ngalesoni, F., Nguyen, C., Nguyen, T. H., Niriayo, Y., Noroozi, M., Owolabi, M., Pereira, David M, Qorbani, M., Rafay, Anwar, Rafiei, A., Rahimi-Movaghar, V., Rai, R., Ram, U., Ranabhat, C., Ray, S. E., Reiner, R., Sadat, Nafis, Sajadi, Haniye Sadat, Santos, J., Sarker, A., Sartorius, B., Satpathy, Maheswar, Savic, M., Schneider, Matthew T., Sepanlou, S., Shaikh, M., Sharif, M., She, Jun, Sheikh, A., Sisay, M., Soneji, S., Soofi, M., Tadesse, H., Tao, Tianchan, Templin, Tara, Tesema, A., Thapa, S., Thomson, A., Tobe-Gai, Ruoyan, Topor-Madry, R., Tran, B., Tran, Khanh B., Tran, T., Undurraga, E., Vasankari, T., Violante, F., Wijeratne, T., Xu, Gelin, Yonemoto, N., Younis, M., Yu, Chuanhua, Zaki, M., Zhou, Lei, Zlavog, Bianca S., Murray, C. (2018)
The Lancet
DOI: 10.1016/S0140-6736(18)30698-6Tools & Outputs
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We're always looking for research partners, health system collaborators, and clinical sites to advance our work in clinical AI evaluation.
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