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A Deep Learning Model for Identifying the Risk of Mesenteric Malperfusion in Acute Aortic Dissection Using Initial Diagnostic Data: Algorithm Development and Validation

A Deep Learning Model for Identifying the Risk of Mesenteric Malperfusion in Acute Aortic Dissection Using Initial Diagnostic Data: Algorithm Development and Validation

For example, a systematic review reported that 25% of patients with lower limb ischemia also experience visceral ischemia, 31% of whom have involvement of the SMA [38], whereas Yang et al [7] reported that renal malperfusion was more common in patients with MMP.

Zhechuan Jin, Jiale Dong, Chengxiang Li, Yi Jiang, Jian Yang, Lei Xu, Ping Li, Zhun Xie, Yulin Li, Dongjin Wang, Zhili Ji

J Med Internet Res 2025;27:e72649

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Large Language Models in Medical Diagnostics: Scoping Review With Bibliometric Analysis

Large Language Models in Medical Diagnostics: Scoping Review With Bibliometric Analysis

Similarly, Yang et al [145] showed race-based tendencies in both GPT-3.5-turbo and GPT-4 models, including generating biased patient backgrounds, associating diseases with specific demographic groups, favoring White patients in treatment recommendations, and showing disparities in projected cost, hospitalization duration, and prognosis [145]. Such biased models could negatively impact the quality of diagnoses that patients receive.

Hankun Su, Yuanyuan Sun, Ruiting Li, Aozhe Zhang, Yuemeng Yang, Fen Xiao, Zhiying Duan, Jingjing Chen, Qin Hu, Tianli Yang, Bin Xu, Qiong Zhang, Jing Zhao, Yanping Li, Hui Li

J Med Internet Res 2025;27:e72062

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