Natural language processing (NLP) is a field of study that focuses on the interactions between human language and computers. It sits at the intersection of computer science, artificial intelligence and computational linguistics. In other words, NLP allows computers to read, analyze and interpret human language. This solution requires a change in people, process and technology. However, there is some reluctance in our industry to embrace new technology, especially when the quality process needs to evolve in tandem. To get a clear view of how Text Mining and NLP can help the automation of clinical trials, let’s get in deep of the most used methods for processing natural language stored in clinical data. Firstly, we recall that the objective is to extract medical concepts and semantic types from both the clinical trial criteria datasets and patient data. Text Mining and Natural Language Processing (NLP) combined, constitute a solid solution for representing this valuable information stored on medical records. They deal both with free text, and the main objective is to extract non-trivial knowledge from it. It encompasses everything from information retrieval to terminology extraction, text classification to spelling correction and sentiment analysis. NLP methods rely intensely on probability theory, statistics and machine learning field. It deals also with linguistics concepts, grammatical structure and the lexicon of words. NLP is a game changer in clinical research because it simplifies and accelerates an outdated process which in turn saves time, money and alleviates burden from your trial teams