About

Ryan Ribeira is a Clinical Assistant Professor in Emergency Medicine at Stanford University and is affiliated with the Center for Artificial Intelligence in Medicine & Imaging (AIMI). His role involves integrating artificial intelligence into medical imaging and emergency medicine, contributing to research and education in these fields. As part of AIMI, he is engaged in advancing AI applications for healthcare, supporting innovative projects, and fostering collaboration between academia and industry to improve medical diagnostics and patient outcomes.

Research topics

• Political Science
• Medicine
• Gerontology
• Computer Science
• Sociology
• Internal medicine
• World Wide Web
• Environmental health
• Pathology
• Gender studies
• Geography
• Psychiatry
• Medical emergency
• Demography
• Psychology

Selected publications

• Adoption of Boarding in Inpatient Hallways During Emergency Department Crowding

  Annals of Emergency Medicine · 2025-07-15

  article
  PublisherDOI

• Stanford Emergency Medicine Partnership Program: a novel approach to streamlining the evaluation and implementation of emerging health technologies through academic–industry partnerships

  BMJ Innovations · 2024-06-12

  articleOpen accessSenior author
  PublisherOA PDFDOI

• The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review

  JMIR Medical Informatics · 2024 · 104 citations

  • Computer Science
  • Medicine
  • Medical emergency

  BACKGROUND: Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM. OBJECTIVE: Given the absence of a comprehensive framework for exploring the roles of LLMs in EM, this scoping review aims to systematically map the existing literature on LLMs' potential applications within EM and identify directions for future research. Addressing this gap will allow for informed advancements in the field. METHODS: Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for papers published between January 2018 and August 2023 that discussed LLMs' use in EM. We excluded other forms of AI. A total of 1994 unique titles and abstracts were screened, and each full-text paper was independently reviewed by 2 authors. Data were abstracted independently, and 5 authors performed a collaborative quantitative and qualitative synthesis of the data. RESULTS: A total of 43 papers were included. Studies were predominantly from 2022 to 2023 and conducted in the United States and China. We uncovered four major themes: (1) clinical decision-making and support was highlighted as a pivotal area, with LLMs playing a substantial role in enhancing patient care, notably through their application in real-time triage, allowing early recognition of patient urgency; (2) efficiency, workflow, and information management demonstrated the capacity of LLMs to significantly boost operational efficiency, particularly through the automation of patient record synthesis, which could reduce administrative burden and enhance patient-centric care; (3) risks, ethics, and transparency were identified as areas of concern, especially regarding the reliability of LLMs' outputs, and specific studies highlighted the challenges of ensuring unbiased decision-making amidst potentially flawed training data sets, stressing the importance of thorough validation and ethical oversight; and (4) education and communication possibilities included LLMs' capacity to enrich medical training, such as through using simulated patient interactions that enhance communication skills. CONCLUSIONS: LLMs have the potential to fundamentally transform EM, enhancing clinical decision-making, optimizing workflows, and improving patient outcomes. This review sets the stage for future advancements by identifying key research areas: prospective validation of LLM applications, establishing standards for responsible use, understanding provider and patient perceptions, and improving physicians' AI literacy. Effective integration of LLMs into EM will require collaborative efforts and thorough evaluation to ensure these technologies can be safely and effectively applied.

  PublisherDOI

• Hospitalization prediction from the emergency department using computer vision AI with short patient video clips

  npj Digital Medicine · 2024-12-19 · 4 citations

  articleOpen access

  In this study, we investigate the performance of computer vision AI algorithms in predicting patient disposition from the emergency department (ED) using short video clips. Clinicians often use "eye-balling" or clinical gestalt to aid in triage, based on brief observations. We hypothesize that AI can similarly use patient appearance for disposition prediction. Data were collected from adult patients at an academic ED, with mobile phone videos capturing patients performing simple tasks. Our AI algorithm, using video alone, showed better performance in predicting hospital admissions (AUROC = 0.693 [95% CI 0.689, 0.696]) compared to models using triage clinical data (AUROC = 0.678 [95% CI 0.668, 0.687]). Combining video and triage data achieved the highest predictive performance (AUROC = 0.714 [95% CI 0.709, 0.719]). This study demonstrates the potential of video AI algorithms to support ED triage and alleviate healthcare capacity strains during periods of high demand.

  PublisherOA PDFDOI

• Precision emergency medicine

  Academic Emergency Medicine · 2024-06-28 · 6 citations

  articleOpen access

  BACKGROUND: Precision health is a burgeoning scientific discipline that aims to incorporate individual variability in biological, behavioral, and social factors to develop personalized health solutions. To date, emergency medicine has not deeply engaged in the precision health movement. However, rapid advances in health technology, data science, and medical informatics offer new opportunities for emergency medicine to realize the promises of precision health. METHODS: In this article, we conceptualize precision emergency medicine as an emerging paradigm and identify key drivers of its implementation into current and future clinical practice. We acknowledge important obstacles to the specialty-wide adoption of precision emergency medicine and offer solutions that conceive a successful path forward. RESULTS: Precision emergency medicine is defined as the use of information and technology to deliver acute care effectively, efficiently, and authentically to individual patients and their communities. Key drivers and opportunities include leveraging human data, capitalizing on technology and digital tools, providing deliberate access to care, advancing population health, and reimagining provider education and roles. Overcoming challenges in equity, privacy, and cost is essential for success. We close with a call to action to proactively incorporate precision health into the clinical practice of emergency medicine, the training of future emergency physicians, and the research agenda of the specialty. CONCLUSIONS: Precision emergency medicine leverages new technology and data-driven artificial intelligence to advance diagnostic testing, individualize patient care plans and therapeutics, and strategically refine the convergence of the health system and the community.

  PublisherOA PDFDOI

• Development and Pilot Evaluation of a Virtual Reality Healthcare Simulation Curriculum for Nursing Education: SLS with VR

  SSRN Electronic Journal · 2023-01-01

  articleOpen accessSenior author
  PublisherDOI

• Integrating Battlefield Documentation into Virtual Reality Medical Simulation Training: Virtual Battlefield Assisted Trauma Distributed Observation Kit (BATDOK)

  Military Medicine · 2023-11-01 · 9 citations

  articleOpen accessSenior author

  INTRODUCTION: Clinical documentation is an essential component of the provision of medical care, enabling continuity of information across provider and site handoffs. This is particularly important in the combat casualty care setting when a single casualty may be treated by four or more or five completely disparate teams across the roles of care. The Battlefield Assisted Trauma Distributed Observation Kit (BATDOK) is a digital battlefield clinical documentation system developed by the Air Force Research Laboratory to address this need. To support the deployment of this tool, we integrated BATDOK into a commercially available virtual reality (VR) medical simulation platform used by the U.S. Air Force and Defense Health Agency personnel in order to provide an immersive simulation training experience which included battlefield documentation. METHODS: A multidisciplinary team consisting of medical educators, VR simulation engineers, emergency physicians and pararescuemen, and BATDOK developers first developed a specification for a virtual BATDOK capability, including a detailed listing of learning objectives, critical interfaces and task plans, and sensor integrations. These specifications were then implemented into the commercially available Virtual Advancement of Learning for Operational Readiness VR Medical Simulation System and underwent developmental testing and evaluation during pararescueman training exercises at the Air Force Special Operations Command Special Operations Center for Medical Integration and Development. RESULTS AND CONCLUSIONS: The BATDOK capability was successfully implemented within the VR Medical Simulation System. The capability consisted of a virtual tablet with replicated interfaces and capabilities based on the developed specifications. These capabilities included integrated point-of-care ultrasound capability, multi-patient management, vitals sign monitoring with sensor pairing and continuous monitoring, mechanism of injury documentation (including injury pattern documentation), intervention logging (including tourniquets, dressing, airways, lines, tubes and drains, splints, fluids, and medications), and event logging. The capability was found to be operational and in alignment with learning objectives and user acceptance goals.

  PublisherOA PDFDOI

• The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review (Preprint)

  2023-10-19

  preprintOpen access

  <sec> <title>BACKGROUND</title> Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM. </sec> <sec> <title>OBJECTIVE</title> Given the absence of a comprehensive framework for exploring the roles of LLMs in EM, this scoping review aims to systematically map the existing literature on LLMs’ potential applications within EM and identify directions for future research. Addressing this gap will allow for informed advancements in the field. </sec> <sec> <title>METHODS</title> Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for papers published between January 2018 and August 2023 that discussed LLMs’ use in EM. We excluded other forms of AI. A total of 1994 unique titles and abstracts were screened, and each full-text paper was independently reviewed by 2 authors. Data were abstracted independently, and 5 authors performed a collaborative quantitative and qualitative synthesis of the data. </sec> <sec> <title>RESULTS</title> A total of 43 papers were included. Studies were predominantly from 2022 to 2023 and conducted in the United States and China. We uncovered four major themes: (1) clinical decision-making and support was highlighted as a pivotal area, with LLMs playing a substantial role in enhancing patient care, notably through their application in real-time triage, allowing early recognition of patient urgency; (2) efficiency, workflow, and information management demonstrated the capacity of LLMs to significantly boost operational efficiency, particularly through the automation of patient record synthesis, which could reduce administrative burden and enhance patient-centric care; (3) risks, ethics, and transparency were identified as areas of concern, especially regarding the reliability of LLMs’ outputs, and specific studies highlighted the challenges of ensuring unbiased decision-making amidst potentially flawed training data sets, stressing the importance of thorough validation and ethical oversight; and (4) education and communication possibilities included LLMs’ capacity to enrich medical training, such as through using simulated patient interactions that enhance communication skills. </sec> <sec> <title>CONCLUSIONS</title> LLMs have the potential to fundamentally transform EM, enhancing clinical decision-making, optimizing workflows, and improving patient outcomes. This review sets the stage for future advancements by identifying key research areas: prospective validation of LLM applications, establishing standards for responsible use, understanding provider and patient perceptions, and improving physicians’ AI literacy. Effective integration of LLMs into EM will require collaborative efforts and thorough evaluation to ensure these technologies can be safely and effectively applied. </sec>

  PublisherDOI

• Changes in low‐acuity patient volume in an emergency department after launching a walk‐in clinic

  Journal of the American College of Emergency Physicians Open · 2023-07-21 · 7 citations

  articleOpen access

  Objective Unscheduled low-acuity care options are on the rise and are often expected to reduce emergency department (ED) visits. We opened an ED-staffed walk-in clinic (WIC) as an alternative care location for low-acuity patients at a time when ED visits exceeded facility capacity and the impending flu season was anticipated to increase visits further, and we assessed whether low-acuity ED patient visits decreased after opening the WIC. Methods In this retrospective cohort study, we compared patient and clinical visit characteristics of the ED and WIC patients and conducted interrupted time-series analyses to quantify the impact of the WIC on low-acuity ED patient visit volume and the trend. Results There were 27,211 low-acuity ED visits (22.7% of total ED visits), and 7,058 patients seen in the WIC from February 26, 2018, to November 17, 2019. Low-acuity patient visits in the ED reduced significantly immediately after the WIC opened (P = 0.01). In the subsequent months, however, patient volume trended back to pre-WIC volumes such that there was no significant impact at 6, 9, or 12 months (P = 0.07). Had WIC patients been seen in the main ED, low-acuity volume would have been 27% of the total volume rather than the 22.7% that was observed. Conclusion The WIC did not result in a sustained reduction in low-acuity patients in the main ED. However, it enabled emergency staff to see low-acuity patients in a lower resource setting during times when ED capacity was limited.

  PublisherOA PDFDOI

• 312 When AI Meets the Emergency Department: Realizing the Benefits of Large Language Models in Emergency Medicine

  Annals of Emergency Medicine · 2023-09-26 · 4 citations

  articleOpen access
  PublisherOA PDFDOI

Frequent coauthors

• Laleh Gharahbaghian

  Stanford University

  12 shared

• Moon O. Lee

  11 shared

• Michael A. Gisondi

  University of Southern California

  10 shared

• Suzanne Lippert

  Kaiser Permanente Oakland Medical Center

  9 shared

• Ewen Wang

  Stanford University

  9 shared

• Yvonne Maldonado

  9 shared

• Maame Yaa A. B. Yiadom

  Stanford University

  9 shared

• Jonathan Altamirano

  Stanford University

  9 shared