Harnessing the power of AI/ML has the potential to transform health tech and medical device companies by providing better outcomes, better patient and provider satisfaction and engagement, and better insight into product performance and real world evidence.
Newly published research provides a valuable method that may be useful for designing unique, functional proteins for therapeutic and diagnostic applications.
Major cloud service providers offer off-the-shelf emotion prediction APIs, but accuracy and interpretability often fail to meet the needs of our clients. Read how our team developed a new approach to predict emotions from video. Here we describe the model building process, its current performance, and future considerations.
Learn about the approach and impact of the model we built to predict skin tone from face images and videos using a unique combination of computer vision and machine learning to help organizations monitor for racial biases in large datasets.
Here is what we see as the most valuable high-impact trends in AI/ML for life sciences and healthcare
Five challenges and solutions critical to the success of Data Science in Healthcare and Life Sciences
“Mercury Data Science has taken a tool it originally developed for COVID-19 research and applied it into new areas of research and innovation.”
Most data is raw and needs to be shaped into usable features that highlight signal and minimize noise. “Feature engineering” is the most important part of real-world data science, paving the path from raw data to valuable insights.
Why companies need to think ahead about infrastructure for data science.
Domain and technical knowledge aren’t sufficient criteria when looking for your new data science hire. We have found that a better characterization of a data scientist’s innate potential is the way they balance creativity and skepticism.