The case of Mayo Clinic: Providing a framework for fair and explainable AI in Radiology
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Hi there,
Mayo Clinic is top-ranked for quality more often than any other health care organization in the world (U.S. News & World Report’s ranking of top hospitals). With 12 000 research studies underway, it is also the health care organization that does the most with AI in the world.
Because in the end, behind AI diagnosis, it is human life that is at stake, Ingedata’s organization strives to meet the specific challenges of annotation in health, in terms of reliability and security of personal data processing.
What do we learn from it?
1. AI developments in Radiology must enter in a scalable framework
2. Monitoring and data security: How to create a safe environment without becoming a bottleneck?
3. Use cases:
a. Median technologies: Facilitating the governance of medical data annotation
b.Quantum surgical: Preparing for model maintenance as early as the model design phase.
4. Specialized teams, the first lever for annotation reliability
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Featured Speaker
Chief Customer Officier, Ingedata
Journalist and moderator
About Ingedata
Ingedata is a leading provider of data annotation to train, validate and optimize Computer Vision and Natural Language Processing models. We are expert in the setup of specialized team and the definition of production workflows, for the annotation of image datasets that are used to train computer vision models.
We hire specialized doctors, general practicioners, nurses, cytopathologists and medical students to annotate data with various levels of complexity. Available workflows allow productivity while ensuring tight quality control and data security.
"An estimated 7 percent of Google’s daily searches are health-related, 70,000 each minute. ”In this case we are organizing the world’s health information and making it accessible to everyone.”
Ingedata have been a great, effective partner and I’m glad we decided to work with them. We can set an annotation process with a team of doctors they form and train and know that the annotation pipeline is more efficient and qualitative. We used to do all of our annotations in-house, and this just wasn't scalable. Ingedata makes the process much easier. I just choose the image dataset, collaborate with Ingedata on the protocol, and launch the project. Thanks, guys!
SAM SEYMOUR PRODUCT MANAGER FOR DATA @ARTERYS