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"Bridging Real-World Evidence Gaps:"

The Role of Literature-Derived RWE in Rare or Complex Indications

This white paper discusses the crucial role of literature-derived Real-World Evidence (RWE) in bridging critical knowledge gaps, particularly for rare or complex indications where traditional RWE sources like Electronic Health Records (EHRs), claims, and registries are often insufficient. The peer-reviewed biomedical literature is a rich, global source of detailed patient data, including demographics, phenotypes, genetics, and outcomes, which complements traditional RWE by increasing data breadth and depth. While the unstructured, vast, and non-standardized nature of the literature presents challenges, these can be overcome by a combinatorial approach leveraging AI for scalable extraction and normalization, coupled with expert curation for quality and regulatory standards. Real-world examples, such as programs for NPM1-mutated Acute Myeloid Leukemia and PRKAG2 Syndrome, demonstrate that this methodology consistently uncovers substantially more patients and richer clinical detail than anticipated. Literature-derived RWE, when systematically curated, powers precision therapeutics programs by optimizing clinical trial design, supporting label expansion, enhancing disease understanding, and improving disease awareness. Genomenon, the author of this paper, specializes in this AI-powered and expertly curated approach to unlock the full potential of RWE from the biomedical literature.

Offered Free by: Emerj Artificial Intelligence Research
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