Resource Hub

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On this page you will find Stanford’s latest AI research in health and medicine, including access to datasets, tools, research papers, and the institutes, centers, and labs leading this work.

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AI Development Resources

Stanford Health Care and Stanford School of Medicine Secure GPT (beta)

Stanford Health Care and Stanford School of Medicine Secure GPT is powered by GPT 4.0 and provides a safe, secure environment that you can use to ask questions, summarize text and files, and help solve a range of complex problems.

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Stanford Medicine Children's Health AskDigi

Stanford Medicine Children's Health AskDigi is a tool designed for SMCH employees to safely process PDF documents, automate the generation and summarization of reports, standardize communications, and extract vital data, all while strictly adhering to our PHI safety standards.

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Stanford Text De-identification Algorithm

Stanford de-identifier was trained on a variety of radiology and biomedical documents with the goal of automatising the de-identification process while reaching satisfactory accuracy for use in production. 

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AI-Ready Clinical Datasets Shared by AIMI

Stanford AIMI shares annotated data to foster transparent and reproducible collaborative research to advance AI in medicine. Datasets are available to the public to view and use without charge for non-commercial research purposes.

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EHRSHOT: An EHR Benchmark for Few-Shot Evaluation and Foundation Models

EHRSHOT contains de-identified structured data from the electronic health records (EHRs) of 6,739 patients from Stanford Medicine. Unlike MIMIC-III/IV and other popular EHR datasets, EHRSHOT is longitudinal and not restricted to ICU/ED patients.

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FactEHR

FactEHR is a dataset consisting of full document fact decompositions for 2,168 clinical notes spanning four types from three hospital systems.

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HARMONI

HARMONI is a three-dimensional (3D) computer vision and audio processing method for analyzing caregiver-child behavior and interaction from observational videos. HARMONI operates at subsecond resolution, estimating 3D mesh representations and spatial interactions of humans, and adapts to challenging natural environments using an environment-targeted synthetic data generation module. Deployed on 500 hours from the SEEDLingS dataset, HARMONI generates detailed quantitative measurements of 3D human behavior previously unattainable through manual efforts or 2D methods.

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INSPECT: A Multimodal Dataset for Pulmonary Embolism Diagnosis and Prognosis

Synthesizing information from various data sources plays a crucial role in the practice of modern medicine. Current applications of artificial intelligence in medicine often focus on single-modality data due to a lack of publicly available, multimodal medical datasets.

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Stanford AIMI Dataset Index

Stanford AIMI has launched a community-driven resource of health AI datasets for machine learning in healthcare as part of a vision to catalyze sharing well curated, de-identified clinical data sets.

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ACCEPT-AI

ACCEPT-AI is a framework of recommendations for the safe inclusion of pediatric data in artificial intelligence and machine learning (AI/ML) research. It has been built on fundamental ethical principles of pediatric and AI research and incorporates age, consent, assent, communication, equity, protection of data, and technological considerations. ACCEPT-AI has been designed to guide researchers, clinicians, regulators, and policymakers and can be utilized as an independent tool, or adjunctively to existing AI/ML guidelines.

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Clinical Excellence Research Center's Computer Vision Initiative

Applies cutting-edge computer vision technologies to efforts to boost the reliability of clinical care. Sensors passively capture data from the clinical environment, while machine-learning algorithms are developed to automatically detect patient and staff activities across a variety of settings from hospital (ICU, OR, units) to outpatient (clinics, home-based care).

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A framework for evaluation Fair, Useful, and Reliable AI Models in healthcare systems (FURM)

The Data Science team at Stanford Health Care has developed a mechanism to identify fair, useful and reliable AI models (FURM) by conducting an ethical review to identify potential value mismatches, simulations to estimate usefulness, financial projections to assess sustainability, as well as analyses to determine IT feasibility, design a deployment strategy, and recommend a prospective monitoring and evaluation plan.


The FURM assessment uses APLUS, a reusable framework for quantitatively assessing via simulation the utility gained from integrating a model into a clinical workflow. Click here to learn more about APLUS.

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Our Partners

Stanford Medicine

A leader in the biomedical revolution, Stanford Medicine has a long tradition of leadership in pioneering research, creative teaching protocols and effective clinical therapies. Stanford Medicine comprises three organizations: Stanford Health Care (SHC), Stanford Medicine Children's Health (SMCH), and Stanford School of Medicine (SoM).

Stanford Institute for Human-Centered Artificial Intelligence

Stanford University's Human-Centered Artificial Intelligence (HAI) collaborates with labs, centers, and institutes across and beyond the Stanford campus to advance AI research, education, and policy to improve the human condition.

Stanford Center for Artificial Intelligence in Medicine & Imaging

The AIMI Center was established to develop, evaluate, and disseminate artificial intelligence systems to benefit patients.  AIMI conducts research that solves clinically important imaging problems using machine learning and other AI techniques.

Stanford Center for Digital Health

In partnership with Silicon Valley innovators as well as colleagues throughout the university-and across fields of engineering, computer science, medicine, business, and the humanities-the Center for Digital Health develops tools and approaches that improve health not only for Stanford patients but for all people worldwide. We prioritize underlying core values of ethics, inclusion, and accessibility, bringing together a global perspective and a rigorous scientific approach.

Coalition for Health AI

The Coalition for Health AI (CHAI) was created to welcome a diverse array of stakeholders to listen, learn, and collaborate to drive the development, evaluation, and appropriate use of AI in healthcare. 

Responsible AI in Healthcare (Shah Lab)

The Shah Lab is focused on bringing AI into clinical use, safely, ethically and cost effectively. Work is organized in two broad work-streams: 1) Creation and adoption of foundation models in medicine and 2) Making machine learning models clinically useful.

Systems Utilization Research for Stanford Medicine

Designed to support improved health system operations and to educate health-system professionals (doctors, nurses, leaders), SURF uses machine learning, mathematical optimization, simulation, and a variety of statistical, probabilistic, and computational tools.

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