Saria, Suchi

Do no harm: a roadmap for responsible machine learning for health care

Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., et al. (2019) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 1,266

Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients

Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014) · Health Affairs

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 1,235

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., et al. (2022) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMeta-analysis and systematic reviews

Cited by 1,039

The Clinician and Dataset Shift in Artificial Intelligence

Finlayson, S. G., Subbaswamy, A., Singh, K., Bowers, J., Kupke, A., Zittrain, J., Kohane, I. S., & Saria, S. (2021) · New England Journal of Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 855

Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist.

Norgeot, B., Quer, G., Beaulieu-Jones, B. K., Torkamani, A., Dias, R., Gianfrancesco, M., Arnaout, R., Kohane, I. S., Saria, S., Topol, E., et al. (2020) · Nature Medicine

Artificial Intelligence in Healthcare and EducationExplainable Artificial Intelligence (XAI)

Cited by 665

Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., et al. (2022) · BMJ

Artificial Intelligence in Healthcare and EducationMeta-analysis and systematic reviews

Cited by 665

A targeted real-time early warning score (TREWScore) for septic shock.

Henry, K. E., Hager, D. N., Pronovost, P. J., & Saria, S. (2015) · Science Translational Medicine

Sepsis Diagnosis and TreatmentAcute Kidney Injury Research

Cited by 598

Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis

Adams, R., Henry, K. E., Sridharan, A., Soleimani, H., Zhan, A., Rawat, N., Johnson, L., Hager, D. N., Cosgrove, S. E., Markowski, A., et al. (2022) · Nature Medicine

Sepsis Diagnosis and TreatmentMachine Learning in Healthcare

Cited by 365

The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence

Sounderajah, V., Guni, A., Liu, X., Collins, G. S., Karthikesalingam, A., Markar, S. R., Golub, R. M., Denniston, A. K., Shetty, S., Moher, D., et al. (2025) · Nature Medicine

Meta-analysis and systematic reviewsArtificial Intelligence in Healthcare and Education

Cited by 352

From development to deployment: dataset shift, causality, and shift-stable models in health AI

Subbaswamy, A., & Saria, S. (2019) · Biostatistics

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 276

Human–machine teaming is key to AI adoption: clinicians’ experiences with a deployed machine learning system

Henry, K. E., Kornfield, R., Sridharan, A., Linton, R. C., Groh, C., Wang, T., Wu, A., Mutlu, B., & Saria, S. (2022) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationElectronic Health Records Systems

Cited by 207

AI, Health, and Health Care Today and Tomorrow: The JAMA Summit Report on Artificial Intelligence

Angus, D. C., Khera, R., Lieu, T., Liu, V., Ahmad, F. S., Anderson, B., Bhavani, S. V., Bindman, A., Brennan, T., Celi, L. A., et al. (2025) · JAMA

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 195

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

Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing

Henry, K. E., Adams, R., Parent, C., Soleimani, H., Sridharan, A., Johnson, L., Hager, D. N., Cosgrove, S. E., Markowski, A., Klein, E. Y., et al. (2022) · Nature Medicine

Sepsis Diagnosis and TreatmentElectronic Health Records Systems

Cited by 144

A Nationwide Network of Health AI Assurance Laboratories

Shah, N. H., Halamka, J. D., Saria, S., Pencina, M., Tazbaz, T., Tripathi, M., Callahan, A., Hildahl, H., & Anderson, B. (2024) · JAMA

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 114

Artificial Intelligence for Social Good

Hager, G. D., Drobnis, A., Fang, F., Ghani, R., Greenwald, A., Lyons, T., Parkes, D. C., Schultz, J., Saria, S., Smith, S. F., et al. (2019) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 108

Development and validation of a machine learning model to predict near-term risk of iatrogenic hypoglycemia in hospitalized patients

Mathioudakis, N. N., Abusamaan, M. S., Shakarchi, A. F., Sokolinsky, S., Fayzullin, S., McGready, J., Zilbermint, M., Saria, S., & Golden, S. H. (2021) · JAMA Network Open

Hyperglycemia and glycemic control in critically ill and hospitalized patientsMachine Learning in Healthcare

Cited by 67

A bias evaluation checklist for predictive models and its pilot application for 30-day hospital readmission models

Wang, H. E., Landers, M., Adams, R., Subbaswamy, A., Kharrazi, H., Gaskin, D. J., & Saria, S. (2022) · Journal of the American Medical Informatics Association

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 63

Measuring Patient Mobility in the ICU Using a Novel Noninvasive Sensor

Ma, A. J., Rawat, N., Reiter, A., Shrock, C., Zhan, A., Stone, A., Rabiee, A., Griffin, S., Needham, D. M., & Saria, S. (2017) · Critical Care Medicine

Context-Aware Activity Recognition SystemsIntensive Care Unit Cognitive Disorders

Cited by 53

High Frequency Remote Monitoring of Parkinson's Disease via Smartphone: Platform Overview and Medication Response Detection

Zhan, A., Little, M. A., Harris, D. A., Abiola, S. O., Dorsey, E. R., Saria, S., & Terzis, A. (2016) · arXiv

Context-Aware Activity Recognition SystemsMobile Health and mHealth Applications

Cited by 50

The promise of AI and technology to improve quality of life and care for older adults

Abadir, P. M., Chellappa, R., Choudhry, N., Demiris, G., Ganesan, D., Karlawish, J., Li, R. M., Moore, J. H., Walston, J. D., AITCs, D. &. A. O. T., et al. (2023) · Nature Aging

Technology Use by Older AdultsArtificial Intelligence in Healthcare and Education

Cited by 24

Comparison of automated sepsis identification methods and electronic health record-based sepsis phenotyping: improving case identification accuracy by accounting for confounding comorbid conditions.

Henry, K. E., Hager, D. N., Osborn, T. M., Wu, A. W., & Saria, S. (2019) · Critical Care Explorations

Sepsis Diagnosis and TreatmentMachine Learning in Healthcare

Cited by 22

1405: ASSESSING CLINICAL USE AND PERFORMANCE OF A MACHINE LEARNING SEPSIS ALERT FOR SEX AND RACIAL BIAS

Adams, R., Henry, K., Soleimani, H., Rawat, N., Saheed, M., Chen, E., Wu, A., & Saria, S. (2022) · Critical Care Medicine

Sepsis Diagnosis and TreatmentMachine Learning in Healthcare

Cited by 5