AI significantly accelerates the study setup phase, reducing months to days. For example, AI automates tasks such as protocol writing and identifying suitable demographics for trials. From creating custom CRFs to analyzing existing ones, AI efficiently handles various aspects of initial study setup requirements.

Predictive analytics helps to make real-time decisions during the execution of clinical trials. It can study the pattern of these roadblocks and predict their occurrence in the future. This helps in mitigation of most risks or side effects and allows stakeholders to plan recruitment and trial design according to the prediction, preventing expensive delays and mid-study changes.

Medical coding is a crucial part of clinical trials and is a time-consuming process. Enter AI, and now hours worth of coding can now be accomplished in a matter of minutes. AI medical coding algorithms can also be trained with millions of biomedical terms for medical context learning using machine learning models.

One of AI’s biggest contributions to clinical trials is remote Source Data Verification. Site monitoring is an expensive and time consuming process. CRAs can now verify source data without travelling to the sites. Sites can upload source data to the AI engine, which extracts this data and compares it with data entered into the EDC. Matched data items are marked as ‘SDV’. The system automatically generates queries on unmatched data and assigns to sites.

AI plays a crucial role in the analysis of Real World Data (RWD) and harmonization of data. With the growing importance of RWD in clinical trials, the harmonization of the same is also becoming a need of the hour. Artificial intelligence automates processes like data cleaning and offers next gen features like NLP and pattern recognition which helps in making most of the large data sets that are created in the process of clinical trials.