Artificial intelligence has emerged as one of the most consequential, and most contested, developments in clinical trial operations. As sponsors, CROs, and technology vendors invest heavily in AI-driven prediction models for feasibility, it is critical to examine if these tools are a fit for the complexity of the task they supposedly address. AI offers contributions in select feasibility applications, yet most currently available tools are hampered by incomplete data architectures, an absence of therapeutic area expertise, and a structural inability to account for the qualitative, human factors that most decisively influence operational success. The appropriate posture is neither uncritical adoption nor reflexive avoidance, but a deliberate, informed framework in which AI serves as a carefully bounded input within an expert-led process.
Read our white paper to learn more about:
- Why the most consequential limitation of current AI feasibility tools is their data
- A domain-by-domain assessment of where AI adds genuine value versus where human expertise remains essential, from patient segmentation and site identification to geographic strategy and investigative grant projections
- The Expert-Empowered Model, a Human-in-the-Loop framework built on three principles for responsible AI integration, and why organizations that invest in both the right data assets and the right human expertise are best positioned to excel
