Transform drug discovery by enabling researchers to rapidly analyze large-scale data sets, design new molecules, and predict the efficacy of potential drug candidates.
With AI, improve patient recruitment by identifying and screening potential participants based on inclusion and exclusion criteria.
AI-assisted surgery is enabling data-driven decision-making via decision support systems and cognitive robotic assistance.
Predict and prevent patient hazards such as fall prevention with remote monitoring sensors within patient rooms saving hospitals time, money, and resources.
A data-centric platform allows you to accelerate your business outcomes with AI. Labelbox is built to help you transform data into advanced AI applications, while delivering enterprise-grade security and governance for all your data.
"With Labelbox, we’re able to consistently produce high-quality data by allowing our team of domain experts and labelers to collaborate more efficiently. The workflow we’ve built queues up all the work for our labelers to create image annotations, which are then sampled and reviewed by experts, and fed into ML models to make better AI diagnoses."
- Miao Zhang, Data Science at Genentech
"Our goal is to achieve a more efficient data pipeline so that given all this rich data that we collecting, we want to provide insights with actionable and trusted feedback that help surgeons improve their performance. We rely on Labelbox to help align our different teams such as our clinical teams and data science teams to ensure that we have a clearly defined ontology. This ensures that all AI data is consistent and provides meaningful value to our models.”- Xi Liu, Data Science at Intuitive Surgical
"We create algorithms that help inform surgeons of relevant and potentially life-saving information during surgery, getting feedback and requirements from top surgeons was an essential part of our process. Presenting a use case to senior leadership with the support of these domain experts goes a long way to getting the green flag. With Labelbox, we are able to show them how it works in real time and build trust in our models."- Ramanan Paramasivan, R&D, Stryker