The Checklist for Artificial Intelligence in Medical Imaging (CLAIM) provides a structured framework for transparent and reproducible reporting of AI studies in medical imaging. Since its introduction in 2020, CLAIM has been widely adopted by researchers, reviewers, and journal editors, but variability in its interpret...
Tugba Akinci D'Antonoli, L. Adams, Amine Amyar et al.· Radiology: Artificial Intell...· 0 citations
Percival is introduced, a CT-native VLM trained on more than 400,000 CT-report pairs from the Penn Medicine BioBank using a dual-encoder symmetric contrastive framework, and evaluated against alternative foundation-model paradigms, indicating that CT-VLMs uncover latent structure aligned with clinical, physiological, a...
C. Beeche, Joonghyun Kim, H. Tavolinejad et al.· npj Digital Medicine· 1 citation
Six major knowledge-gap domains in applying artificial intelligence in clinical settings are identified, including ensuring that clinical AI supports diagnostic decision-making, integrates into clinical workflows, and mitigates risks related to automation bias, overreliance, fragmentation of care, and unintended conseq...
E. Patterson, Grayson L. Baird, David Bates et al.· Diagnosis· 0 citations
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