Over the last few years, talking about artificial intelligence (AI) and machine learning (ML) could drive customer interest, make something sound more innovative or even appear to boost a company’s valuation. Unfortunately, many of these AI/ML talking points were general, potentially premature and not yet ready to offer results. The clinical research industry was not immune to these discussions and may largely still view many AI and ML approaches as something on the horizon rather than as something available today.
In my time as a CEO in the clinical trial platform arena, I have seen how clinical research studies continue to become more complex, focus on more targeted disease areas and cost more than ever before. In parallel to this evolution, the clinical research industry has a goal of doing more—conducting more targeted studies, enrolling more global participants more inclusively, gathering more data, etc. Achieving these goals in the face of more complexity with less money and fewer people requires the industry to eliminate as many inefficiencies as possible.