Sudha Jayaraman, MD, MSc, FACS is a practicing double-boarded surgeon and ICU physician with a background in public health and more than 15 years of experience in clinical care, health systems research, device and health tech and global health. She serves as the Medical Director for Clinical Strategy and Research at Suki, one of the leading ambient AI companies in the United States. She is also an Adjunct Professor of Surgery and Biomedical Engineering at the University of Utah where she was a full Professor for 5 years and held the Clifford C. Snyder, MD Far Eastern Presidential Endowed Chair. Dr. Jayaraman has authored more than 110 scientific publications and book chapters, delivered over 200 lectures at international, national, and regional conferences, and mentored more than 80 individuals across all stages of training—spanning multiple professions, disciplines, institutions, and countries. She has been funded by the NIH for the last decade and has led a team that developed and implemented a novel digital health platform for emergency communication and coordination in East Africa. She also is an expert reviewer for the NIH, UK’s NIHR, the University of Utah’s Digital Health Initiative Seed Grants and the NIH Fogarty Global Health Emerging Scholars (GHES) fellowship program. Dr Jayaraman is deeply committed to transforming healthcare through technology.
A Conceptual Model for Ambient AI Adoption: Perspectives From Academia and Industry
Josh Biro, Jesse M. Pines, Sudha Jayaraman, Vamsi Chagari, Raj Ratwani · JMIR Medical Informatics · Jun 29, 2026
Unlabelled: Ambient AI technologies are increasingly marketed as solutions to reduce clinician burden and improve care efficiency; however, real-world performance varies widely across clinical settings. Health care provider organizations face challenges in determining which aspects of ambient AI performance matter most and how to obtain meaningful information about those aspects from vendors or through internal evaluation. This article presents a shared mental model to guide health system leaders in conceptualizing ambient AI performance across 3 interdependent dimensions: technical, interface...
https://doi.org/10.2196/91098
Safety of Patient-Facing Agentic AI: a Consensus Framework for Risk Assessment and Mitigation (Preprint)
Sudha Jayaraman, Arooshi Kumar, Erin Palm, Melanie Fernando, Chethan Sarabu, Michael Choma, Kevin Wang, Vishnu Ravi, et al. · Jun 26, 2026
UNSTRUCTURED Deploying agentic AI systems without adequate plans for human supervision raises serious concerns about patient safety, privacy, and equity. To address this gap, a group of experts across industry, academic and clinical informatics interested in AI and safety convened a voice AI taskforce to discuss and develop consensus on the impact of agentic AI in healthcare. Through this collaboration, we developed a consensus framework to determine potential risks and plan mitigation efforts based on potential clinical use cases to aid health care delivery organizations assess, implement and...
https://doi.org/10.2196/preprints.105596
Surgeons at the Policy Table
Denis A. Foretia, Sudha Jayaraman, Michael S. Nussbaum · Annals of Surgery · May 25, 2026
https://doi.org/10.1097/sla.0000000000007102
Validation of a mobile application for prehospital transport time data collection in Kigali, Rwanda
Teresa Bell, Jean Marie Uwitonze, Michael Oravic, Rebecca Maine, Jeanne d’Arc Nyinawankusi, Andrea Davis, Jean Nepomuscene Sindikubwabo, Robert Rickard, et al. · BMC Global and Public Health · Apr 22, 2026
BACKGROUND: Emergency medical services (EMS) are critical for patient outcomes during emergencies. Organised EMS systems that can promptly locate patients, deliver on-scene care, and transport patients to appropriate facilities are lacking in low- and middle-income countries. Through a long-standing partnership with Rwanda's Service d'Aide Médicale d'Urgence (SAMU), we developed and implemented an electronic application to capture prehospital times in Kigali, Rwanda. This study validates the accuracy of electronically captured data and establishes baseline response times for emergencies in Kig...
https://doi.org/10.1186/s44263-026-00271-0
Data quality and associations over 4 years of use of WHO’s Clinical Registry - Trauma Module, in Rwanda
Indrakantha Welgama, Jean Nepomuscene Ntezimana, Irene Bagahirwa, Venantie Umuhoza, Fabrice Iradukunda, D. Lange, Leila Ghalichi, Sudha Jayaraman, et al. · medRxiv · Jan 25, 2026
Abstract Objective Poor quality of injury care causes avoidable death and disability. Quality data is integral to care-quality improvement. We assessed longitudinal data quality of the Trauma Module (TM) of WHO’s Clinical Registry in Rwanda, towards understanding data collection challenges in low resourced settings. Methods Retrospective analysis of TM data between its implementation in August 2019 and July 2023. We describe trends in overall TM case and variable missingness and their associations with availability of funding and formal training. Variable missingness is also described by each ...
https://doi.org/10.64898/2026.01.24.26344755
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