Our perspective on AL/ML in the life sciences industry.
In an industry as dynamic and complex as life sciences, the need for technological innovation is constant. With the increasing demand for faster clinical trials
Taking a closer look at hallucinations, why they occur, and how to limit them, especially if you’re in the process of training an AI model.
Taking a closer look at the intricacies and considerations necessary for successful ML model selection and training.
Taking a closer look at each of the four steps within the training phase of AI Model Training.
We’ll be covering the best practices for training AI models for life sciences, from data preparation to training methods, and more.
Here at Saama, we are continuously innovating, researching, and developing AI models for healthcare. Saama’s AI Research Lab is an in-house team of dedicated AI
When introducing AI to any part of clinical trials, its benefits and advantages often overshadow the very important considerations to make about its implementation. While
When we think of AI within clinical trials, we tend to focus on its benefits and use cases for data aggregation, management, and analysis. While
Within clinical data management (CDM), there are numerous data quality, structure, volume, and collection challenges that make the process overly complex and difficult to oversee.
