Revolutionizing Clinical Trials: A Manifesto for AI-Driven Transformation
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Abstract (arXiv)
This manifesto represents a collaborative vision forged by leaders in pharmaceuticals, consulting firms, clinical research, and AI. It outlines a roadmap for two AI technologies - causal inference and digital twins - to transform clinical trials, delivering faster, safer, and more personalized outcomes for patients. By focusing on actionable integration within existing regulatory frameworks, we propose a way forward to revolutionize clinical research and redefine the gold standard for clinical trials using AI.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 30 references that are in the map. First posted on arXiv 10 Jun 2025.
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7 of 30 references.
References · 7
- 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 287
- 2019Estimating Treatment Effects with Causal Forests: An Applicationcited by 58
- 2016Double/Debiased Machine Learning for Treatment and Causal Parameterscited by 43
- 2024Automatically Learning Hybrid Digital Twins of Dynamical Systemscited by 14
- 2024ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inferencecited by 12
- 2024Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environmentscited by 11
- 2024Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learningcited by 8
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