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TRV-2026-0914Certified recordPeer-reviewed

AI-Enabled Real-World Evidence in Oncology: A Statistical Perspective for Regulatory Decisions

Artificial intelligence (AI) has the potential to strengthen real-world evidence (RWE) for regulatory decision-making, but its contribution varies by application and methodological maturity. RWE remains limited by challenges in data quality, population selection, treatment characterization, outcome assessment, and statistical methodology. Machine learning and generative AI (genAI), combined with causal inference frameworks, may address these challenges. We review applications, limitations, including reproducibil…

Policy · G Space — documented gain · certified 2026-08-28 · v1 · article view · machine-readable

Current reading — gain

Machine learning and generative AI combined with causal inference frameworks may strengthen oncology real-world evidence used for regulatory decisions.

What this doesn’t fix

Contribution varies by application and methodological maturity, with unresolved issues around reproducibility, bias, transportability, uncertainty quantification, and regulatory acceptability for fit-for-purpose use.

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Truvace Impact Record TRV-2026-0914, v1: “AI-Enabled Real-World Evidence in Oncology: A Statistical Perspective for Regulatory Decisions.” Truvace, 2026-08-28. /record/TRV-2026-0914 (accessed at citation time). sha256 6c373f4b0c68c591…

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