Integrating Artificial Intelligence, Circulating Tumor DNA, and Real-World Evidence to Optimize Hematologic Clinical Trials: Toward Adaptive and Learning Trial Designs

Abstract

The integration of emerging technologies and real-world data is transforming the landscape of hematologic clinical trials. Artificial intelligence (AI) offers remarkable capabilities for predictive modeling, patient stratification, and adaptive trial design, while circulating tumor DNA (ctDNA) provides a minimally invasive biomarker for disease monitoring, the early detection of relapse, and treatment response assessment. Concurrently, real-world evidence (RWE) complements traditional clinical trial data by capturing treatment effectiveness, safety, and patient outcomes in broader, heterogeneous populations. This review examines the synergistic potential of AI, ctDNA, and RWE to optimize trial design and decision-making in hematologic malignancies. We discuss methodological innovations, including AI-driven patient selection, ctDNA-guided adaptive interventions, and the incorporation of RWE for external control arms and post-marketing surveillance. Key challenges, such as data standardization, regulatory considerations, and ethical implications, are also addressed. By integrating these advanced tools, clinical trials in hematology can achieve greater efficiency, precision, and translatability, ultimately accelerating the development of personalized therapies and improving patient outcomes.

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