Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Automated Speech-recognition Anatomic Pathology (ASAP) reporting.
9
Zitationen
4
Autoren
1994
Jahr
Abstract
Artificial intelligence speech-recognizers integrated with Laboratory information and Telefaxcommunication Systems have allowed for totally automated surgical pathology reporting. Automated Speech-Recognition Anatomic Pathology (ASAP) reporting improves the speed, text accuracy, comprehensiveness, and workflow management of diagnostic reports while eliminating support personnel. Healthcare reform goals of increased productivity and economy are furthered. Reports are rendered "as soon as possible" (ASAP) expediting appropriate clinical management and decreased length of stay and hospital costs.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.239 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.095 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.463 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 · 5.428 Zit.