The Role of AI Across the Clinical Trial Landscape

Clinical trials face hurdles in patient selection, recruitment, and retention, causing delays and insufficient participation. Eligibility screening is costly and yields few eligible volunteers. Phase III trials generate vast data, necessitating increased recruitment budgets.

The solution is to harness AI’s potential. AI can optimise patient recruitment, predict treatment efficacy, automate data analysis, and enhance safety monitoring. This streamlines trials, reduces costs and improves data quality. AI also aids in identifying disease targets and designing targeted treatments with fewer side effects, 

The aim should be to make trials accessible to more patients by leveraging AI’s power, rather than relying solely on patient initiative,

Join this discussion to find solutions to these pertinent questions and pave the way for better adoption of AI in clinical trials: 

• How can AI improve participant selection, adherence, and retention in clinical trials? 

• In what ways can the utilisation of AI’s predictive model for participant selection and stratification in clinical trials facilitate the development of more targeted and efficacious therapeutic interventions?

• What are the potential biases and limitations of AI algorithms in clinical trials & how can we mitigate them?

• What have you learned so far from your use of AI in clinical trials?