Revolutionizing Medicine: Unleashing the Power of Real-World Data and AI in Advancing Clinical Trials

In the biopharmaceutical industry, the conventional "linear and sequential" clinical trial approach is still the norm; however, it is frequently beset by issues with patient selection, retention, and monitoring that are not up to par, leading to longer trial durations and higher trial failure rates. Artificial intelligence (AI) has the potential to reduce the duration of clinical trials significantly, expedite protocol design and study implementation, improve trial outcomes, and lower the cost of biopharmaceutical R&D. AI may also speed up clinical cycle times. Clinical trial mining, design and execution, and real-world experience analysis are all possible using AI. Major drug makers are utilizing AI to locate volunteers for clinical trials or to cut down on the number of subjects required for drug testing, which might speed up medication development and save millions of dollars. The clinical trial procedure may be enhanced by AI, but evidentiary requirements for a drug's efficacy and safety won't alter, according to regulators. This article discusses the application of AI, RWD, and RWE in clinical trials and how it is transforming the biopharma value chain, encompassing all pharmaceutical firms referred to as "biopharma companies" due to the inclusion of biologics in their development pipelines and within these biopharma firms, artificial intelligence (AI) is finding its way into drug research.