Artificial intelligence presents a significant opportunity to improve the planning and execution of clinical trials. It can address key clinical feasibility challenges, such as trial design, protocol optimization and smarter country and site selection. In fact, various AI solutions are being explored for their potential to improve clinical trial development processes.
However, while the potential of AI in clinical trials is massive, there are also some challenges that hinder its successful adoption. These challenges include:
Data access and quality
Healthcare data is crucial if you want to use AI for clinical trials, but it is also a headache. It can be hard to obtain, is usually unstructured, and riddled with incomplete information. Making this data easier to access and of better quality is essential for AI to work well.
Skill gap
Another major barrier is the lack of internal expertise. There is a big gap between the demand and supply of professionals who have knowledge in both data science and clinical development strategy. This is why the existing roles in the industry will also need to adapt, as professionals will be required to work with AI models and check the accuracy of their results.
Internal data maturity
Companies already have some data inside their systems that can help in clinical trials. However, this data needs to be more organized, visible and reliable to be truly useful.
Data integration
Combining data from different places smoothly and in a way that actually makes sense can really help AI work better, especially when there is tons of information to handle. Still, there is a need for solid IT support to connect these data sources properly and ensure everything works together.
Scalability
AI models often work well initially during small pilot projects, but often face challenges when scaled to real-world use. This is a crucial step that can help in their widespread adoption, and hence must be planned from the very beginning.
Change management
To integrate generative AI in life sciences, organizations must transform how data-driven decisions are made across the enterprise. They need to shift from relying mainly on leader-driven decisions to embracing data-driven decision-making. However, this will require strong efforts to help people accept and adapt to the new approach.
While these challenges are real, they are not impossible to overcome. With the right foundations in place, AI will become a game-changer, making clinical trials run smoother, more predictably and patient-centric.
