Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Evaluation of electronic health record-integrated artificial intelligence chart review
0
Zitationen
9
Autoren
2026
Jahr
Abstract
Abstract This study evaluates the quality of artificial intelligence (AI) clinical note summarization by analyzing physician qualitative feedback on a large language model (LLM) chart review tool integrated into the electronic health record (EHR). Physicians provided free-text feedback on AI-generated chart summaries, which physician informaticists analyzed using MAXQDA. Feedback from 10 physicians was collected on 147 AI-generated summaries. Positive feedback was common ( n = 71), but users identified omissions ( n = 46), confusing content ( n = 20), token limitations ( n = 27), hallucinations ( n = 5), and bias ( n = 1). Cohen’s Kappa was 0.64, indicating substantial reviewer agreement. Physician feedback on the tool revealed overall positive impressions, though omissions raised concerns about summary completeness. AI-assisted chart review technology is not infallible, but physicians found this tool acceptable for use in clinical workflows.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.292 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.143 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.539 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.452 Zit.