Generation and Evaluation of Realistic Synthetic Clinical Progress Notes for Prostate Cancer using Large Language Models.
Rey-Blanes, A.; Veredas-Morente, J.; Vivas-Vargas, E.; Gil-Garcia, F.; Moreno-Barea, F. J.; Veredas, F. J.
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Background and Objective: Access to real-world electronic health records (EHRs) remains limited by privacy, governance and annotation constraints, hindering the development of clinical natural language processing models. Realistic synthetic progress notes may provide EHR-like corpora that preserve clinically rigorous information on diagnoses, treatments, symptoms, imaging, laboratory findings and therapeutic trajectories without relying directly on sensitive patient records. This study evaluates whether large language models (LLMs) can generate realistic Spanish prostate cancer progress notes from published case reports, preserving clinical content, temporality and hospital-style conventions.
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