Bedoya, Armando

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

"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

Integrating a Machine Learning System Into Clinical Workflows: Qualitative Study

Sandhu, S., Lin, A. L., Brajer, N., Sperling, J., Ratliff, W., Bedoya, A. D., Balu, S., O'Brien, C., & Sendak, M. P. (2020) · Journal of Medical Internet Research

Artificial Intelligence in Healthcare and EducationElectronic Health Records Systems

Cited by 168

Minimal Impact of Implemented Early Warning Score and Best Practice Alert for Patient Deterioration*

Bedoya, A. D., Clement, M. E., Phelan, M., Steorts, R. C., O’Brien, C., & Goldstein, B. A. (2019) · Critical Care Medicine

Healthcare Technology and Patient MonitoringElectronic Health Records Systems

Cited by 162

Use of artificial intelligence in critical care: opportunities and obstacles

Pinsky, M. R., Bedoya, A., Bihorac, A., Celi, L., Churpek, M., Economou-Zavlanos, N. J., Elbers, P., Saria, S., Liu, V., Lyons, P. G., et al. (2024) · Critical Care

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 160

A framework for the oversight and local deployment of safe and high-quality prediction models

Bedoya, A. D., Economou-Zavlanos, N. J., Goldstein, B. A., Young, A., Jelovsek, J. E., O’Brien, C., Parrish, A. B., Elengold, S., Lytle, K., Balu, S., et al. (2022) · Journal of the American Medical Informatics Association

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 119

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

Overcoming barriers to the adoption and implementation of predictive modeling and machine learning in clinical care: what can we learn from US academic medical centers?

Watson, J., Hutyra, C. A., Clancy, S. M., Chandiramani, A., Bedoya, A., Ilangovan, K., Nderitu, N., & Poon, E. G. (2020) · JAMIA Open

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 95

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