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We evaluated artificial intelligence (AI) for detecting osteoradionecrosis, fibrosis, trismus, and dysphagia in 207 head and neck cancer patient electronic health records. After adjudication and fine-tuning, accuracy reached 87% (F1 = 0.92). The model processed 20,835 sentences within seconds, demonstrating feasibility and efficiency for automating the identification of radiation-related late toxicities.
Liu et al. (Wed,) studied this question.