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Evaluation of Large Language Models in Tailoring Educational Content for Cancer Survivors and Their Caregivers: Quality Analysis

Evaluation of Large Language Models in Tailoring Educational Content for Cancer Survivors and Their Caregivers: Quality Analysis

GPT-4’s high adherence to word limits and GPT-4 Turbo’s better compliance to reading level compliance proved their ability to meet our requirements when tailoring content. Prompting GPTs to produce bulleted-format content is likely to result in better educational content compared with textual-format content. All models exhibit strong capability in generating highly relevant content. However, they fall short in terms of completeness.

Darren Liu, Xiao Hu, Canhua Xiao, Jinbing Bai, Zahra A Barandouzi, Stephanie Lee, Caitlin Webster, La-Urshalar Brock, Lindsay Lee, Delgersuren Bold, Yufen Lin

JMIR Cancer 2025;11:e67914