David Kim

Assistant Professor

Stanford University

United States

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About

David Kim is an Assistant Professor at Stanford University in the United States. His research focuses on the integration of artificial intelligence in clinical care, continuous monitoring systems, and patient interaction methodologies in emergency medicine. Recent articles authored by Dr. Kim explore topics such as atrial fibrillation screening, multimodal clinical monitoring, and the use of smartphone accelerometry for patient rehabilitation.

Articles (16)

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2025

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2025

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2025

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2025

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2024

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2023

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2023

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2023

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2022

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2022

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2022

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2020

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2020

Collaborators (4)

James Quinn

Professor

Stanford University School of Medicine

UNITED STATES

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Gabrielle Bunney

Assistant Professor

Stanford University

UNITED STATES

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grant lipman

Clinical Professor

Stanford University

UNITED STATES

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Brian Suffoletto

Stanford University

UNITED STATES

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David Kim

Research Interests

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Articles (16)

Opportunistic Screening for Atrial Fibrillation With Continuous ECG Monitoring in the Emergency Department

MC-MED, multimodal clinical monitoring in the emergency department

Evaluation of a text message + pedometer intervention to increase steps after emergency department discharge: a pilot study

An evaluation framework for clinical use of large language models in patient interaction tasks

Feasibility of Measuring Smartphone Accelerometry Data During a Weekly Instrumented Timed Up-and-Go Test After Emergency Department Discharge: Prospective Observational Cohort Study

GPT-4 accurately classifies the clinical actionability of emergency department imaging reports

Development and external validation of a pretrained deep learning model for the prediction of non-accidental trauma

Maximizing Equity in Acute Coronary Syndrome Screening across Sociodemographic Characteristics of Patients

Predicting patient decompensation from continuous physiologic monitoring in the emergency department

Association of Physician Malpractice Claims Rates with Admissions for Low-Risk Chest Pain

A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring

Transfer learning enables prediction of myocardial injury from continuous single-lead electrocardiography

Development and Comparative Performance of Physiologic Monitoring Strategies in the Emergency Department

Predicting malnutrition from longitudinal patient trajectories with deep learning

Rates of Co-infection Between SARS-CoV-2 and Other Respiratory Pathogens

A body bag can save your life: a novel method of cold water immersion for heat stroke treatment

Collaborators (4)

James Quinn

Gabrielle Bunney

grant lipman

Brian Suffoletto

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