Transforming Length-of-Stay Modeling for ICU Admissions: Enhancing Clinical Application Through Sequential Prediction Approaches

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Chang et al. compared a vision transformer (ViT) against human endoscopists in predicting ulcerative colitis activity using white-light colonoscopy videos.
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The study raises concerns about the alignment of the ViT architecture with clinical objectives and data representation for ulcerative colitis.
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Label generation for histologic healing from endoscopy is complex due to spatial imprecision and noise, complicating the classification process.
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Clinical translation of AI models requires evaluation metrics beyond accuracy, including model calibration and decision-curve analysis.
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Future research should focus on developing robust, interpretable, and uncertainty-aware AI systems for endoscopic assessments in clinical workflows.

Digital Health

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