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#clinical-workflows News & Analysis

2 articles tagged with #clinical-workflows. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles
AIBullisharXiv – CS AI · Jun 237/10
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Human and AI collaboration for pulmonary nodule segmentation

Hi-Seg, a human-in-the-loop segmentation framework built on the Segment Anything Model, achieved 85% accuracy in pulmonary nodule detection across 1,179 patients, outperforming five state-of-the-art AI models by 10-22%. The research demonstrates that non-experts with brief training can match junior medical professionals' performance, suggesting foundation models can be safely integrated into clinical workflows while reducing annotator burden.

AINeutralarXiv – CS AI · Jun 25/10
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Examine Clinicians' Modification of Hedging Language in Ambient AI Documentation: A Comparative Study of AI Drafts and Final Notes

A study analyzing how clinicians edit ambient AI-generated clinical notes reveals that physicians systematically introduce more hedging language (uncertainty qualifiers) rather than remove it, indicating they tend toward greater caution when revising AI drafts. The findings show substantial variation across AI vendors and medical specialties, highlighting inconsistent AI documentation quality and clinician confidence levels.