arXiv:2608. 16643v1 Announce Type: cross Abstract: Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that evaluate each clinical note in isolation.
By Yifan Zhang, Rahmatollah Beheshti
arXiv:2604. 05435v2 Announce Type: replace Abstract: Incomplete or inconsistent discharge documentation drives care fragmentation and avoidable readmissions.
By Akshat Dasula, Prasanna Desikan, Jaideep Srivastava, Shivali Dalmia, Abhishek Mukherji
Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that evaluate each clinical note in isolation. Error-detection benchmarks are typically constructed by injecting errors into notes, such that each erroneous note has a natural counterpart.
arXiv:2601. 22025v2 Announce Type: replace-cross Abstract: Evaluating Large Language Model (LLM) applications differs from conventional software testing because outputs are probabilistic, semantically variable, and sensitive to prompt and model changes.
By Daniel Commey
arXiv:2607. 26929v1 Announce Type: cross Abstract: The same diagnostic result can support or challenge one causal claim yet fail to address another when the claims concern different populations, outcomes, estimands, pathways, or identifying assumptions.
By Weiyi Kong, Zhuoran Li
arXiv:2607. 13036v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for decision support in healthcare, but clinical evidence is often incomplete or evolving.
By Oriana Presacan, Andreea Grama, Larisa Irimin\u{a}, Alireza Nik, Jaya Ojha, Vajira Thambawita, Ciprian I. B\u{a}cil\u{a}, Bogdan Ionescu, Michael A. Riegler