Limited impact of real-world evidence in medical oncology: how can AI turn the tide
Real-world evidence (RWE), derived from real-world health data (RWD) generated during routine clinical practice, is increasingly recognized as a valuable complement to traditional clinical trial data in medical oncology. While randomized controlled trials remain the gold standard for evidence-based...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
1 October 2025
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| In: |
European journal of cancer
Year: 2025, Jahrgang: 228, Pages: 1-3 |
| ISSN: | 1879-0852 |
| DOI: | 10.1016/j.ejca.2025.115714 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.ejca.2025.115714 Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S0959804925004976 
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| Verfasserangaben: | Anna Pellat, Jakob Nikolas Kather |
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| 520 | |a Real-world evidence (RWE), derived from real-world health data (RWD) generated during routine clinical practice, is increasingly recognized as a valuable complement to traditional clinical trial data in medical oncology. While randomized controlled trials remain the gold standard for evidence-based medicine, they often face limitations due to rising costs, ethical constraints, and the inability to cover all treatment combinations and diverse patient populations. The current impact of RWE is limited by several challenges, including data inconsistency, quality issues, and burdensome data collection processes. Despite its role in specific areas like accelerating drug development in rare cancers, RWE has yet to achieve its full potential due to these limitations. Artificial intelligence (AI), particularly through natural language processing and large language models (LLMs), presents a transformative opportunity for RWE. AI technologies can streamline data collection, extraction, cleaning, and analysis, making the process more efficient and accurate. LLMs, for instance, can extract structured data from unstructured text and facilitate the integration of diverse data sources. This automation can significantly reduce the workload on clinicians and improve the consistency and reliability of RWD. These tools can address existing challenges and unlock the full potential of RWD, ultimately leading to improved patient outcomes and more robust oncology research. | ||
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