Stanford University and VinBrain - To design a state-of-the-art AI-enabled platform to advance medical diagnosis and treatments
- Written by Media Outreach
Signing Ceremony of Data Use Agreement with Stanford University (photo: VinBrain)
To design a state-of-the-art AI-enabled platform for more accurate diagnosis and treatments, a tremendous amount of geographically distributed data, medical images, and patient medical records are required for system training.
Stanford University will share data with VinBrain for model training and testing models to boost efficiency in Radiology reports
The DUA grants Stanford University and VinBrain the collaborative power to improve efficiency in radiology reports and advance AI in healthcare by sharing relevant de-identified data for model training and testing, while tackling complexity, ambiguity, and limitations in medical image interpretation.Starting with 240,000 anonymized medical images and report pairs provided by Stanford through this DUA, VinBrain will work to improve the accuracy of the RadGraph method through a research collaboration with Stanford to extract clinical entities and relation annotations from a large dataset of full-text radiology reports.
With this strong support from Stanford, VinBrain aims to raise the bar for research on AI solutions by collaborating beyond Vietnam to improve product quality, meanwhile, continuing to add and develop the most massive data source in Vietnam to date, with more than 2.3 million images being analyzed through the DrAid™ platform so far.
Mr. Steven Truong affirmed: "VinBrain always strictly adheres to data security regulations according to USA standards." He also emphasized that Stanford sharing data with VinBrain demonstrates Stanford's absolute trust in VinBrain's commitment to data privacy and security. The DUA signed with Stanford University is not an overnight result but a long journey of 3+ years of collaboration. This collaboration has produced many research projects together that have achieved desirable clinical outcomes.
Professor Curtis Langlotz, Director of the Center for Artificial Intelligence in Medicine and Stanford University also expressed his enthusiasms, "I remember the first time I met Steven at RSNA Spotlight organized by Stanford near San Francisco Airport. It was the beginning of this wonderful relationship that developed over time. We appreciate VinBrain's leadership in research and the capabilities that they have. The speed of work is incredible, and I also appreciate the collaborative spirit of VinBrain." He added, "I know that we've done a lot, but there's still a lot more to do. I look forward to continued collaborations. I'm glad we sat down together to sign this Agreement."
This Agreement promises to comprehensively expand cooperation in the next areas of development, strengthen the relationship between VinBrain and the world's leading University, and take VinBrain further in the research of AI-developed solutions for worldwide patients.
About RadGraph: a novel method introduced by Prof. Pranav Rajpurkar (Harvard University) and Prof. Curtis Langlotz at Stanford. This collaboration worksbetween Stanford University and VinBrain to address complexity, ambiguity, and limitations for interpreting medical images.
About DrAid™: a platform of comprehensive artificial intelligence (AI) doctor assistant software developed by VinBrain to assist medical teams and doctors in diagnosing heart, lung, liver, and bone diseases based on digital medical images. The platform is evaluated to have superior efficiency compared to traditional methods in observing and analyzing medical images, helping in reducing overload and improving early detection in diagnosis.
- DrAid Radiology V1: An AI-enabled triage and notification software designed to aid the clinical assessment of adult Chest X-ray cases with features suggestive of pneumothorax. This software is FDA 510(K) cleared and apt for the US market.
- DrAid™ Appliance: an AI-enabled system to classify an adult Chest X-ray between normal and nearly 60 abnormal findings with higher-than-average quality standards of primary care physicians
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