Artificial Intelligence in Clinical Trials

Traditional'linear and sequential' clinical trials remain the accepted way to ensure the efficacy and safety of new medicines. However, the lengthy tried and tested process of discrete and fixed phases of randomised controlled trials (RCTs) was designed principally for testing mass-market drugs and has changed little in recent decades (figure 1).1 RCTs lack the analytical power, flexibility and speed required to develop complex new therapies that target smaller and often heterogeneous patient populations. In addition, suboptimal patient selection, recruitment and retention, together with difficulties managing and monitoring patients effectively, are contributing to high trial failure rates and raising the costs of research and development.2 Over the past few years, biopharma companies have been able to access increasing amounts of scientific and research data from a variety of sources, known collectively as real-world data (RWD). However, they have often lacked the skills and technologies to enable them to utilise this data effectively.