Artificial Intelligence allows a machine to simulate human intelligence to solve problems. AI aims to develop an intelligent system that can perform complex tasks. AI systems use logic and decision trees to learn, reason, and self-correct.

Machine Learning allows a machine to learn autonomously from past data. ML aims to build machines that can learn from data to increase the accuracy of the output. ML systems rely on statistical models to learn and can self-correct when provided with new data.

We use AI to analyze vast datasets from the U.S. Food and Drug Administration, delivering critical insights into regulatory trends, drug approvals, and more.  By tapping into the FDA's data, we provide stakeholders with a real-time, AI-driven roadmap to successful submissions and regulations.

We use Machine Learning as a building block for our AI models. Leveraging its power to sift through vast quantities of FDA data efficiently and effectively. By harnessing the potential of ML, we can train our algorithms to recognize intricate patterns, correlations, and potential predictors of FDA decisions that traditional analytical methods might miss.

AI is the broader concept of enabling a machine or system to sense, reason, act, or adapt like a human. ML is an application of AI that allows machines to extract knowledge from data and learn from it autonomously.