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. Secure log-in required.

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. Secure log-in required.

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. Access the associated GitHub repository here and the manuscript published in the Journal of the American Medical Informatics Association here.

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.

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.

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.

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.

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).