Gao, Michael

The genetic architecture of down syndrome phenotypes revealed by high-resolution analysis of human segmental trisomies

Korbel, J. O., Tirosh-Wagner, T., Urban, A. E., Chen, X.-N., Kasowski, M., Dai, L., Grubert, F., Erdman, C., Gao, M. C., Lange, K., et al. (2009) · Proceedings of the National Academy of Sciences

Down syndrome and intellectual disability researchGenomic variations and chromosomal abnormalities

Cited by 399

Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study

Sendak, M. P., Ratliff, W., Sarro, D., Alderton, E., Futoma, J., Gao, M., Nichols, M., Revoir, M., Yashar, F., Miller, C., et al. (2020) · JMIR Medical Informatics

Sepsis Diagnosis and TreatmentMachine Learning in Healthcare

Cited by 287

Presenting machine learning model information to clinical end users with model facts labels

Sendak, M. P., Gao, M., Brajer, N., & Balu, S. (2020) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationExplainable Artificial Intelligence (XAI)

Cited by 279

"The human body is a black box": supporting clinical decision-making with deep learning

Sendak, M., Elish, M. C., Gao, M., Futoma, J., Ratliff, W., Nichols, M., Bedoya, A., Balu, S., & O'Brien, C. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 196

A Path for Translation of Machine Learning Products into Healthcare Delivery

Sendak, M., D’Arcy, J., Kashyap, S., Gao, M. C., Nichols, M., Corey, K., Ratliff, W., & Balu, S. (2020) · EMJ Innovations

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 172

Machine learning for early detection of sepsis: an internal and temporal validation study

Bedoya, A. D., Futoma, J., Clement, M. E., Corey, K., Brajer, N., Lin, A., Simons, M. G., Gao, M., Nichols, M., Balu, S., et al. (2020) · JAMIA Open

Sepsis Diagnosis and TreatmentMachine Learning in Healthcare

Cited by 109

Development and preliminary testing of health equity across the AI lifecycle (HEAAL): a framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities

Kim, J. Y., Hasan, A., Kellogg, K. C., Ratliff, W., Murray, S. G., Suresh, H., Valladares, A., Shaw, K., Tobey, D., Vidal, D. E., et al. (2024) · PLOS Digital Health

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 61

Translating ethical and quality principles for the effective, safe and fair development, deployment and use of artificial intelligence technologies in healthcare

Economou-Zavlanos, N. J., Bessias, S., Cary, M. P., Bedoya, A. D., Goldstein, B. A., Jelovsek, J. E., O’Brien, C. L., Walden, N., Elmore, M., Parrish, A. B., et al. (2024) · Journal of the American Medical Informatics Association

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 57

Tuning insulin receptor signaling using de novo-designed agonists

Wang, X., Cardoso, S., Cai, K., Venkatesh, P., Hung, A., Ng, M., Hall, C., Coventry, B., Lee, D. S., Chowhan, R., et al. (2025) · Molecular Cell

Metabolism, Diabetes, and CancerProtein Structure and Dynamics

Cited by 24

Evaluating sepsis watch generalizability through multisite external validation of a sepsis machine learning model.

Valan, B., Prakash, A., Ratliff, W., Gao, M., Muthya, S., Thomas, A., Eaton, J. L., Gardner, M., Nichols, M., Revoir, M., et al. (2025) · npj Digital Medicine

Sepsis Diagnosis and TreatmentMachine Learning in Healthcare

Cited by 21

The algorithm journey map: a tangible approach to implementing AI solutions in healthcare

Boag, W., Hasan, A., Kim, J. Y., Revoir, M., Nichols, M., Ratliff, W., Gao, M., Zilberstein, S., Samad, Z., Hoodbhoy, Z., et al. (2024) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 19