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Investigating Clinicians’ Intentions and Influencing Factors for Using an Intelligence-Enabled Diagnostic Clinical Decision Support System in Health Care Systems: Cross-Sectional Survey

Investigating Clinicians’ Intentions and Influencing Factors for Using an Intelligence-Enabled Diagnostic Clinical Decision Support System in Health Care Systems: Cross-Sectional Survey

We conducted the study in 3 tertiary hospitals (Shanghai Children’s Hospital, Ren Ji Hospital, and Shanghai Sixth People’s Hospital) in Shanghai. The study involved administering a questionnaire survey to 247 clinicians across the inpatient and outpatient departments of the 3 hospitals. The study spanned a duration of 4 months, from December 2023 to March 2024.

Rui Zheng, Xiao Jiang, Li Shen, Tianrui He, Mengting Ji, Xingyi Li, Guangjun Yu

J Med Internet Res 2025;27:e62732

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

In addition, both Xiao et al’s and Asthana et al’s studies [36,37] evaluated the performance of fine-tuned LLMs in nonclinical environments. Their results highlighted the significant potential of LLMs in summarizing general text through the adoption of advanced fine-tuning techniques.

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