Manual abstraction of real-world data (RWD) from unstructured health records (HRs) remains resource-intensive, error-prone, and highly variable across institutions. Large language models (LLMs) offer a scalable alternative, but their performance in multicenter oncology settings is not fully validated.
Next-Generation Multicenter Studies: Using Artificial Intelligence to Automatically Process Unstructured Health Records of Patients with Lung Cancer across Multiple Institutions
Annals of Oncology | | M. Aldea, L. Zullo, V. Levrat, J. Bennouna, S. Schneider, O. Mercier, E. Mougenot, E. Bergot, C. Dujon, N. Cloarec, C.Audigier Valette, A. Nuccio, M. Deloger, C. Helissey, S. Simon, A. Carpentier, A. Djarallah, P. Rolland, J.C. Louis, L. Ancillon, B. Vignal, F. Rambaud, P. Tessier, L. Chuttoo, K. Siby, A. Poplu, K. Zarca, S. Michiels, F. Barlesi, F. Le Ouay, B. Besse
Topics: lung-cancer, blood-cancer