Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Surgical data science and artificial intelligence for surgical education
72
Zitationen
6
Autoren
2021
Jahr
Abstract
Surgical data science (SDS) aims to improve the quality of interventional healthcare and its value through the capture, organization, analysis, and modeling of procedural data. As data capture has increased and artificial intelligence (AI) has advanced, SDS can help to unlock augmented and automated coaching, feedback, assessment, and decision support in surgery. We review major concepts in SDS and AI as applied to surgical education and surgical oncology.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.197 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.047 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.410 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 · 5.410 Zit.
Autoren
Institutionen
- Massachusetts General Hospital(US)
- Centre National de la Recherche Scientifique(FR)
- Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'Imagerie(FR)
- Université de Strasbourg(FR)
- Institut de Chirurgie Guidée par l'Image(FR)
- Agostino Gemelli University Polyclinic(IT)
- Istituti di Ricovero e Cura a Carattere Scientifico(IT)
- University Health Network(CA)
- Institut de Recherche contre les Cancers de l’Appareil Digestif(FR)