Balu, Suresh

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

Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): a retrospective, single-site study

Corey, K. M., Kashyap, S., Lorenzi, E., Lagoo-Deenadayalan, S. A., Heller, K., Whalen, K., Balu, S., Heflin, M. T., McDonald, S. R., Swaminathan, M., et al. (2018) · PLOS Medicine

Machine Learning in HealthcareCardiac, Anesthesia and Surgical Outcomes

Cited by 204

"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

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

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

The Project Baseline Health Study: a step towards a broader mission to map human health

Arges, K., Assimes, T., Bajaj, V., Balu, S., Bashir, M. R., Beskow, L., Blanco, R., Califf, R., Campbell, P., Carin, L., et al. (2020) · npj Digital Medicine

Health, Environment, Cognitive AgingArtificial Intelligence in Healthcare and Education

Cited by 82

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

Organizational Governance of Emerging Technologies: AI Adoption in Healthcare

Kim, J. Y., Boag, W., Gulamali, F., Hasan, A., Hogg, H. D. J., Lifson, M., Mulligan, D., Patel, M., Raji, I. D., Sehgal, A., et al. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 55

Barriers to Achieving Economies of Scale in Analysis of EHR Data: A Cautionary Tale

Sendak, M. P., Balu, S., & Schulman, K. A. (2017) · Applied Clinical Informatics

Electronic Health Records SystemsMachine Learning in Healthcare

Cited by 45

Establishing organizational AI governance in healthcare: a case study in Canada

Kim, J. Y., Hasan, A., Kueper, J., Tang, T., Hayes, C., Fine, B., Balu, S., & Sendak, M. (2025) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 42

Frameworks for procurement, integration, monitoring, and evaluation of artificial intelligence tools in clinical settings: A systematic review

Khan, S. D., Hoodbhoy, Z., Raja, M. H. R., Kim, J. Y., Hogg, H. D. J., Manji, A. A. A., Gulamali, F., Hasan, A., Shaikh, A., Tajuddin, S., et al. (2024) · PLOS Digital Health

Artificial Intelligence in Healthcare and EducationElectronic Health Records Systems

Cited by 36

Enabling collaborative governance of medical AI

Price, W. N., Sendak, M., Balu, S., & Singh, K. (2023) · Nature Machine Intelligence

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 27

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

Development and Validation of ML-DQA -- a Machine Learning Data Quality Assurance Framework for Healthcare

Sendak, M., Sirdeshmukh, G., Ochoa, T., Premo, H., Tang, L., Niederhoffer, K., Reed, S., Deshpande, K., Sterrett, E., Bauer, M., et al. (2022) · arXiv

Data Quality and ManagementElectronic Health Records Systems

Cited by 10

Building models, building capacity: A review of participatory machine learning for HIV prevention

Sendak, M., Young, M., Kim, J. Y., Hasan, A., Kelsey, C., O’Neal, C., Jagneaux, T., Wilbright, W., Couk, J., Lim, S., et al. (2025) · PLOS Global Public Health

Ethics and Social Impacts of AIHIV/AIDS Research and Interventions

Cited by 2